A Multiwavelength Comparison of the Growth of Supermassive Black Holes and Their Hosts in Galaxy Clusters
Dissertation
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University
By
David W. Atlee
Graduate Program in Astronomy
The Ohio State University 2011
Dissertation Committee: Professor L. Paul Martini, Advisor Professor Christopher S. Kochanek Professor David H. Weinberg Copyright by
David W. Atlee
2011 ABSTRACT
I present the results of a mid infrared (MIR) survey of X ray point sources in
8 low z galaxy clusters. I combine visible wavelength observations with MIR data from the Spitzer Space Telescope to construct spectral energy distributions (SEDs).
These SEDs form the basis of all the results presented here.
From SEDs fit to the photometry, I measure galaxy stellar masses and star formation rates (SFRs), and I identify AGN based on the observed shapes of the
SEDs. I also estimate the expected X ray luminosities of the host galaxies of X ray point sources based on their measured stellar masses and SFRs, and I identify sources whose observed X ray luminosities show a significant excess as AGN. The two techniques return very different samples, and only 8 of the 44 identified AGN fall in both samples. The host galaxies of the two AGN samples differ significantly in their specific SFRs: the hosts of IR AGN have much larger sSFR than the X ray
AGN hosts. However, the AGN samples show similar distributions of SFRs and have indistinguishable SFR–M˙ BH relations. This suggests that the difference between the
IR and X ray AGN is driven by the gas fraction in the host galaxies, and the IR
ii AGN would be observed as X ray AGN if the host galaxy did not introduce any absorption.
The AGN show no significant bias in R/R200 compared to the positions of cluster galaxies as a whole. This distinguishes them from star forming galaxies
(SFGs), which show a strong preference to be located away from the cluster center.
A partial correlation analysis shows that this trend is more closely related to R/R200 than to Σ10, which suggests that the SFR–density relation in clusters is driven by gas processes rather than by interactions between individual galaxies. The radial dependence of SF R is consistent with expectations from gas starvation within large observational uncertainties, and it is at least partially driven by changes in the SFRs of individual galaxies. This is indicated both by variations in sSFR ∗ among SFGs with R/R200 and by marginal variations in LT IR of cluster SFGs as a function of R/R200. These variations in the population of SFGs suggests that the transition timescale is > 400 Myr, which is intermediate between the timescales ∼ expected for ram pressure stripping and gas starvation. The observations suggest that gas starvation plays a greater role than ram pressure stripping, but further work is needed. One possible avenue for future work is to examine the evolution in SFRs of cluster galaxies as a function of redshift. This probes the timescale for clusters to end star formation in galaxies as they fall in from the field.
iii This volume is dedicated to my parents, who taught me the value of
both hard work and a sunny afternoon.
iv ACKNOWLEDGMENTS
A body of work with the scope of the one presented here is impossible without contributions from many individuals. First and foremost, I must thank my adviser,
Dr. Paul Martini. His patience and attention to detail have allowed me to produce a far better dissertation than would otherwise have been possible. I am also deeply indebted to the outstanding Astronomers with whom it has been my priviledge to collaborate over the years. I especially want to thank Drs. Chris Kochanek and John
Mulchaey, who have not only provided invaluable advice and direction, but who also gave generously of their own time to help me secure a job. The entire Ohio State
Astronomy Department, from the department chair to the newest graduate students have always provided stimulation and collegiality through various functions, most notably morning coffee, and for that I thank them. Finally, I cannot end without acknowledging the invaluable contribution made by my family and my girlfriend with their love, undestanding, and unwavering support over these many years. Sine qua sum non.
v VITA
October 2, 1982 ...... Born – Norristown, Pennsylvania, United States
2005 ...... B.S. Astronomy & Astrophysics B.S. Mathematics B.S. Physics, with Honors in Physics The Pennsylvania State University
2006 – 2008 ...... Dean’s Distinguished University Fellow The Ohio State University
2008 – 2010 ...... Graduate Teaching and Research Associate Department of Astronomy The Ohio State University
2010 – 2011 ...... Dean’s Distinguished University Fellow The Ohio State University
PUBLICATIONS
Research Publications
1. D. W. Atlee, P. Martini, R. J. Assef, D. D. Kelson, and J. S. Mulchaey, “A Multi wavelength Study of Low Redshift Clusters of Galaxies I: Comparison of X ray and Mid Infrared Selected Active Galactic Nuclei”, ApJ, 729, 22, (2011).
2. K. D. Denney, B. M. Peterson, R. W. Pogge, A. Adair, D. W. Atlee, K. Au Yong, M. C. Bentz, J. C. Bird, D. J. Brokofsky, E. Chisholm, M. L. Comins, M. Dietrich, V. T. Doroshenko, J. D. Eastman, Y. S. Efimov, S. Ewald, S. Ferbey, C. M. Gaskell, C. H. Hedrick, K. Jackson, S. A. Klimanov, E. S. Klimek, A. K. Kruse, A. Lad´eroute, J. B. Lamb, K. Leighly, T. Minezaki, S. V. N azarov, C. A. Onken, E. A.Petersen, P. Peterson, S. Poindexter, Y. Sakata, K. J. Schlesinger, S. G. Sergeev, N. Skolski, L. Stieglitz, J. J. Tobin, C. Unterborn, M. Vestergaard, A. E.
vi Watkins, L. C. Watson, and Y. Yoshii, “Reverberation Mapping Measurements of Black Hole Masses in Six Local Seyfert Galaxies”, ApJ, 721, 715, (2010).
3. C. Villforth, K. Nilsson, J. Heidt, L. O. Takalo, T. Pursimo, A. Berdyu gin, E. Lindfors, M. Pasanen, M. Winiarski, M. Drozdz, W. Ogloza, M. Kurpinska Winiarska, M. Siwak, D. Koziel Wierzbowska, C. Porowski, A. Kuzmicz, J. Krzesinski, T. Kundera, J. H. Wu, X. Zhou, Y. Efimov, K. Sadakane, M. Kamada, J. Ohlert, V. P. Hentunen, M. Nissinen, M. Dietrich, R. J. Assef, D. W. Atlee, J. Bird, D. L. Depoy, J. Eastman, M. S. Peeples, J. Prieto, L. Watson, J. C. Yee, A. Liakos, P. Niarchos, K. Gazeas, S. Dogru, A. Donmez, D. Marchev, S. A. Coggins Hill, A. Mattingly, W. C. Keel, S. Haque, A. Aungwerojwit, and N. Bergvall, “Variability and stability in blazar jets on time scales of years: optical polarization monitoring of OJ 287 in 2005 2009”, MNRAS, 402 2087, (2010).
4. C. B. D’Andrea, M. Sako, B. Dilday, J. A. Frieman, J. Holtzman, R. Kessler, K. Konishi, D. P. Schneider, J. Sollerman, J. C. Wheeler, N. Yasuda, D. Cinabro, S. Jha, R. C. Nichol, H. Lampeitl, M. Smith, D. W. Atlee, B. Bassett, F. J. Castander, A. Goobar, R. Miquel, J. Nordin, L. Ostman,¨ J. L. Prieto, R. Quimby, A. G. Riess, and M. Stritzinger, “Type II P Supernovae from the SDSS II Supernova Survey and the Standardized Candle Method”, ApJ, 708, 661, (2010).
5. K. D. Denney, B. M. Peterson, R. W. Pogge, A. Adair, D. W. Atlee, K. Au Yong, M.C. Bentz, J. C. Bird, D. J. Brokofsky, E. Chisholm, M. L. Comins, M. Dietrich, V. T. Doroshenko, J. D. Eastman, Y. S. Efimov, S. Ewald, S. Ferbey, C. M. Gaskell, C. H. Hedrick, K. Jackson, S. A. Klimanov, E. S. Klimek, A. K. Kruse, A. Lad´eroute, J. B. Lamb, K. Leighly, T. Minezaki, S. V. Nazarov, C. A. Onken, E. A. Petersen, P. Peterson, S. Poindexter, Y. Sakata, K. J. Schlesinger, S. G. Sergeev, N. Skolski, N. Stieglitz, J. J. Tobin, C. Unterborn, M. Vestergaard, A. E. Watkins, L. C. Watson, and Y. Yoshii, “Diverse Kinematic Signatures from Reverberation Mapping of the Broad Line Region in AGNs”, ApJ, 704, L80, (2009).
6. D. W. Atlee, and S. Mathur, “GALEX Measurements of the Big Blue Bump in Soft X ray selected Active Galactic Nucleus”, ApJ, 703, 1597, (2009).
7. K. D. Denney, L. C. Watson, B. M. Peterson, R. W. Pogge, D. W. Atlee, M. C. Bentz, J. C. Bird, D. J. Brokofsky, M. L. Comins, M. Dietrich, V. T. Doroshenko, J. D. Eastman, Y. S. Efimov, C. M. Gaskell, C. H. Hedrick, S. A. Klimanov, E. S. Klimek, A. K. Kruse, J. B. Lamb, K. Leighly, T. Minezaki, S. V. Nazarov, E. A. Petersen, P. Peterson, S. Poindexter, Y. Schlesinger, K. J. Sakata, S. G. Sergeev, J. J. Tobin, C. Unterborn, M. Vestergaard, A. E. Watkins, and Y. Yoshii, “A Revised Broad line Region Radius and Black Hole Mass for the Narrow line Seyfert 1 NGC 4051”, ApJ, 702, 1353, (2009).
vii 8. M. J. Valtonen, K. Nilsson, C. Villforth, H. J. Lehto, L. O. Takalo, E. Lindfors, A. Sillanp¨a¨a, V. P. Hentunen, S. Mikkola, S. Zola, M. Drozdz, D. Koziel, W. Ogloza, M. Kurpinska Winiarska, M. Siwak, M. Winiarski, J. Heidt, M. Kidger, T. Pursimo, J. H. Wu, X. Zhou, K. Sadakane, D. Marchev, M. Nissinen, P. Niarchos, A. Liakos, K. Gazeas, S. Dogru, G. Poyner, M. Dietrich, R. Assef, D. Atlee, J. Bird, D. DePoy, J. Eastman, M. Peeples, J. Prieto, L. Watson, J. Yee, A. Mattingly, and J. Ohlert “Tidally Induced Outbursts in OJ 287 during 2005 2008”, ApJ, 698, 781, (2009).
9. D. W. Atlee, R. J. Assef, and C. S. Kochanek, “Evolution of the UV Ex cess in Early Type Galaxies”, ApJ, 694, 1539, (2009).
10. C. J. Grier, B. M. Peterson, M. C. Bentz, K. D. Denney, J. D. East man, M. Dietrich, R. W. Pogge, J. L. Prieto, D. L. DePoy, R. J. Assef, D. W. Atlee, J. Bird, M. E. Eyler, M. S. Peeples, R. Siverd, L. C. Watson, and J. C. Yee, “The Mass of the Black Hole in the Quasar PG 2130+099”, ApJ, 688, 837, (2008).
11. S. Frank, M. C. Bentz, K. Z. Stanek, S. Mathur, M. Dietrich, B. M. Pe terson, and D. W. Atlee, “Disparate MG II absorption statistics towards quasars and gamma ray bursts: a possible explanation”, Ap&SS, 312, 325, (2007).
12. D. W. Atlee, and A. Gould, “Photometric Selection of QSO Candidates from GALEX Sources”, ApJ, 644, 53, (2007).
13. A. Achterberg, M. Ackermann, J. Adams, J. Ahrens, K. Andeen, D. W. Atlee, J. N. Bahcall, X. Bai, B. Baret, S. W. Barwick, R. Bay, K. Beattie, T. Becka, J. K. Becker, K. H. Becker, P. Berghaus, D. Berley, E. Bernardini, D. Bertrand, D. Z. Besson, E. Blaufuss, D. J. Boersma, C. Bohm, J. Bolmont, S. B¨oser, O. Botner, A. Bouchta, J. Braun, C. Burgess, T. Burgess, T. Castermans, D. Chirkin, B. Christy, J. Clem, D. F. Cowen, M. V. D’Agostino, A. Davour, C. T. Day, C. de Clercq, L. Demir¨ors, F. Descamps, P. Desiati, T. DeYoung, J. C. Diaz Velez, J. Dreyer, J. P. Dumm, M. R. Duvoort, W. R. Edwards, R. Ehrlich, J. Eisch, R. W. Ellsworth, P. A. Evenson, O. Fadiran, A. R. Fazely, T. Feser, K. Filimonov, B. D. Fox, T. K. Gaisser, J. Gallagher, R. Ganugapati, H. Geenen, L. Gerhardt, A. Goldschmidt, J. A. Goodman, R. Gozzini, S. Grullon, A. Groß, R. M. Gunasingha, M. Gurtneer, A. Hallgren, F. Halzen, K. Han, K. Hanson, D. Hardtke, R. Hardtke, T. Harenberg, J. E. Hart, T. Hauschildt, D. Hays, J. Heise, K. Helbing, M. Hellwig, P. Herquet, G. C. Hill, J. Hodges, K. D. Hoffman, B. Hommez, K. Hoshina, D. Hubert, B. Hughey, P. O. Hulth, K. Hultqvist, S. Hundertmark, J. P. H¨ulß, A. Ishihara, J. Jacobsen, G. S. Japaridze, H. Johansson, A. Jones, J. M. Joseph, K. H. Kampert, A. Karle, H. Kawai, J. L. Kelley, M. Kestel, N. Kitamura, S. R. Klein,
viii S. Klepser, G. Kohnen, H. Kolanoski, M. Kowalski, L. K¨opke, M. Krasberg, K. Kuehn, H. Landsman, H. Leich, D. Leier, M. Leuthold, I. Liubarsky, J. Lundberg, J. L¨unemann, J. Madsen, K. Mase, H. S. Matis, T. McCauley, C. P. McParland, A. Meli, T. Messarius, P. M´esz´aros, H. Miyamoto, A. Mokhtarani, T. Montaruli, A. Morey, R. Morse, S. M. Movit, K. M¨unich, R. Nahnhauer, J. W. Nam, P. Nießen, D. R. Nygren, H. Ogelman,¨ A. Olivas, S. Patton, C. Pe˜na Garay, C. P´erez de Los Heros, A. Piegsa, D. Pieloth, A. C. Pohl, R. Porrata, J. Pretz, P. B. Price, G. T. Przybylski, K. Rawlins, S. Razzaque, E. Resconi, W. Rhode, M. Ribordy, A. Rizzo, S. Robbins, P. Roth, C. Rott, D. Rutledge, D. Ryckbosch, H. G. Sander, S. Sarkar, S. Schlenstedt, T. Schmidt, D. Schneider, D. Seckel, S. H. Seo, S. Seunarine, A. Silvestri, A. J. Smith, M. Solarz, C. Song, J. E. Sopher, G. M. Spiczak, C. Spiering, M. Stamatikos, T. Stanev, P. Steffen, T. Stezelberger, R. G. Stokstad, M. C. Stoufer, S. Stoyanov, E. A. Strahler, T. Straszheim, K. H. Sulanke, G. W. Sullivan, T. J. Sumner, I. Taboada, O. Tarasova, A. Tepe, L. Thollander, S. Tilav, M. Tluczykont, P. A. Toale, D. Turˇcan, N. van Eijndhoven, J. Vandenbroucke, A. van Overloop, B. Voigt, W. Wagner, C. Walck, H. Waldmann, M. Walter, Y. R. Wang, C. Wendt, C. H. Wiebusch, G. Wikstr¨om, D. R. Williams, R. Wischnewski, H. Wissing, K. Woschnagg, X. W. Xu, G. Yodh, S. Yoshida, and J. D. Zornoza, “Five years of searches for point sources of astrophysical neutrinos with the AMANDA II neutrino telescope”, PRD, 75, 102001, (2007).
14. A. Achterberg, M. Ackermann, J. Adams, J. Ahrens, K. Andeen, D. W. Atlee, J. N. Bahcall, X. Bai, B. Baret, M. Bartelt, S. W. Barwick, R. Bay, K. Beattie, T. Becka, J. K. Becker, K. H. Becker, P. Berghaus, D. Berley, E. Bernardini, D. Bertrand, D. Z. Besson, E. Blaufuss, D. J. Boersma, C. Bohm, J. Bolmont, S. B¨oser, O. Botner, A. Bouchta, J. Braun, C. Burgess, T. Burgess, T. Castermans, D. Chirkin, B. Christy, J. Clem, D. F. Cowen, M. V. D’Agostino, A. Davour, C. T. Day, C. de Clercq, L. Demir¨ors, F. Descamps, P. Desiati, T. DeYoung, J. C. Diaz Velez, J. Dreyer, J. P. Dumm, M. R. Duvoort, W. R. Edwards, R. Ehrlich, J. Eisch, R. W. Ellsworth, P. A. Evenson, O. Fadiran, A. R. Fazely, T. Feser, K. Filimonov, B. D. Fox, T. K. Gaisser, J. Gallagher, R. Ganugapati, H. Geenen, L. Gerhardt, A. Goldschmidt, J. A. Goodman, R. Gozzini, S. Grullon, A. Groß, R. M. Gunasingha, M. Gurtneer, A. Hallgren, F. Halzen, K. Han, K. Hanson, D. Hardtke, R. Hardtke, T. Harenberg, J. E. Hart, T. Hauschildt, D. Hays, J. Heise, K. Helbing, M. Hellwig, P. Herquet, G. C. Hill, J. Hodges, K. D. Hoffman, B. Hommez, K. Hoshina, D. Hubert, B. Hughey, P. O. Hulth, K. Hultqvist, S. Hundertmark, J. P. H¨ulß, A. Ishihara, J. Jacobsen, G. S. Japaridze, A. Jones, J. M. Joseph, K. H. Kampert, A. Karle, H. Kawai, J. L. Kelley, M. Kestel, N. Kitamura, S. R. Klein, S. Klepser, G. Kohnen, H. Kolanoski, L. K¨opke, M. Krasberg, K. Kuehn, H. Landsman, H. Leich, I. Liubarsky, J. Lundberg, J. Madsen, K. Mase, H. S. Matis, T. McCauley, C. P. McParland, A. Meli, T. Messarius, P. M´esz´aros, H. Miyamoto, A. Mokhtarani, T. Montaruli, A. Morey, R. Morse, S. M. Movit, K. M¨unich, R. Nahnhauer, J. W. Nam, P. Nießen,
ix D. R. Nygren, H. Ogelman,¨ Ph. Olbrechts, A. Olivas, S. Patton, C. Pe˜na Garay, C. P´erez de Los Heros, A. Piegsa, D. Pieloth, A. C. Pohl, R. Porrata, J. Pretz, P. B. Price, G. T. Przybylski, K. Rawlins, S. Razzaque, F. Refflinghaus, E. Resconi, W. Rhode, M. Ribordy, A. Rizzo, S. Robbins, P. Roth, C. Rott, D. Rutledge, D. Ryckbosch, H. G. Sander, S. Sarkar, S. Schlenstedt, T. Schmidt, D. Schneider, D. Seckel, S. H. Seo, S. Seunarine, A. Silvestri, A. J. Smith, M. Solarz, C. Song, J. E. Sopher, G. M. Spiczak, C. Spiering, M. Stamatikos, T. Stanev, P. Steffen, T. Stezel berger, R. G. Stokstad, M. C. Stoufer, S. Stoyanov, E. A. Strahler, T. Straszheim, K. H. Sulanke, G. W. Sullivan, T. J. Sumner, I. Taboada, O. Tarasova, A. Tepe, L. Thollander, S. Tilav, P. A. Toale, D. Turˇcan, N. van Eijndhoven, J. Vandenbroucke, A. van Overloop, B. Voigt, W. Wagner, C. Walck, H. Waldmann, M. Walter, Y. R. Wang, C. Wendt, C. H. Wiebusch, G. Wikstr¨om, D. R. Williams, R. Wischnewski, H. Wissing, K. Woschnagg, X. W. Xu, G. Yodh, S. Yoshida, and J. D. Zornoza, “Limits on the High Energy Gamma and Neutrino Fluxes from the SGR 1806 20 Gi ant Flare of 27 December 2004 with AMANDA II Detector”, PRL, 97, 221101, (2006).
15. A. Achterberg, M. Ackermann, J. Adams, J. Ahrens, D. W. Atlee, J. N. Bahcall, X. Bai, B. Baret, M. Bartelt, S. W. Barwick, R. Bay, K. Beattie, T. Becka, J. K. Becker, K. H. Becker, P. Berghaus, D. Berley, E. Bernardini, D. Bertrand, D. Z. Besson, E. Blaufuss, D. J. Boersma, C. Bohm, J. Bolmont, S. B¨oser, O. Botner, A. Bouchta, J. Braun, C. Burgess, T. Burgess, T. Castermans, D. Chirkin, J. Clem, B. Collin, J. Conrad, J. Cooley, D. F. Cowen, M. V. D’Agostino, A. Davour, C. T. Day, C. de Clercq, P. Desiati, T. DeYoung, J. Dreyer, M. R. Duvoort, W. R. Edwards, R. Ehrlich, J. Eisch, R. W. Ellsworth, P. A. Evenson, A. R. Fazely, T. Feser, K. Filimonov, T. K. Gaisser, J. Gallagher, R. Ganugapati, H. Geenen, L. Gerhardt, A. Goldschmidt, J. A. Goodman, M. G. Greene, S. Grullon, A. Groß, R. M. Gunasingha, A. Hallgren, F. Halzen, K. Han, K. Hanson, D. Hardtke, R. Hardtke, T. Harenberg, J. E. Hart, T. Hauschildt, D. Hays, J. Heise, K. Helbing, M. Hellwig, P. Herquet, G. C. Hill, J. Hodges, K. D. Hoffman, K. Hoshina, D. Hubert, B. Hughey, P. O. Hulth, K. Hultqvist, S. Hundertmark, A. Ishihara, J. Jacobsen, G. S. Japaridze, A. Jones, J. M. Joseph, K. H. Kampert, A. Karle, H. Kawai, J. L. Kelley, M. Kestel, N. Kitamura, S. R. Klein, S. Klepser, G. Kohnen, H. Kolanoski, L. K¨opke, M. Krasberg, K. Kuehn, H. Landsman, R. Lang, H. Leich, M. Leuthold, I. Liubarsky, J. Lundberg, J. Madsen, K. Mase, H. S. Matis, T. McCauley, C. P. McParland, A. Meli, T. Messarius, P. M´esz´aros, R. H. Minor, P. Mioˇcinovi´c, H. Miyamoto, A. Mokhtarani, T. Montaruli, A. Morey, R. Morse, S. M. Movit, K. M¨unich, R. Nahnhauer, J. W. Nam, P. Nießen, D. R. Nygren, H. Ogelman,¨ Ph. Olbrechts, A. Olivas, S. Patton, C. Pe˜na Garay, C. P´erez de Los Heros, D. Pieloth, A. C. Pohl, R. Porrata, J. Pretz, P. B. Price, G. T. Przybylski, K. Rawlins, S. Razzaque, F. Refflinghaus, E. Resconi, W. Rhode, M. Ribordy, S. Richter, A. Rizzo, S. Robbins, C. Rott, D. Rutledge, D. Ryckbosch, H. G. Sander, S. Schlenstedt, D. Schneider, D. Seckel, S. H. Seo, S. Seunarine, A. Silvestri, A. J. Smith, M. Solarz,
x C. Song, J. E. Sopher, G. M. Spiczak, C. Spiering, M. Stamatikos, T. Stanev, P. Steffen, D. Steele, T. Stezelberger, R. G. Stokstad, M. C. Stoufer, S. Stoyanov, K. H. Sulanke, G. W. Sullivan, T. J. Sumner, I. Taboada, O. Tarasova, A. Tepe, L. Thollander, S. Tilav, P. A. Toale, D. Turˇcan, N. van Eijndhoven, J. Vandenbroucke, B. Voigt, W. Wagner, C. Walck, H. Waldmann, M. Walter, Y. R. Wang, C. Wendt, C. H. Wiebusch, G. Wikstr¨om, D. R. Williams, R. Wischnewski, H. Wissing, K. Woschnagg, X. W. Xu, G. Yodh, S. Yoshida, J. D. Zornoza, and P. L. Biermann “On the selection of AGN neutrino source candidates for a source stacking analysis with neutrino telescopes”, Astroparticle Physics, 26, 282, (2006).
16. A. Achterberg, M. Ackermann, J. Adams, J. Ahrens, K. Andeen, D. W. Atlee, J. N. Bahcall, X. Bai, B. Baret, M. Bartelt, S. W. Barwick, R. Bay, K. Beattie, T. Becka, J. K. Becker, K. H. Becker, P. Berghaus, D. Berley, E. Bernardini, D. Bertrand, D. Z. Besson, E. Blaufuss, D. J. Boersma, C. Bohm, J. Bolmont, S. B¨oser, O. Botner, A. Bouchta, J. Braun, C. Burgess, T. Burgess, T. Castermans, D. Chirkin, B. Christy, J. Clem, D. F. Cowen, M. V. D’Agostino, A. Davour, C. T. Day, C. de Clercq, L. Demir¨ors, F. Descamps, P. Desiati, T. DeYoung, J. C. Diaz Velez, J. Dreyer, J. P. Dumm, M. R. Duvoort, W. R. Edwards, R. Ehrlich, J. Eisch, R. W. Ellsworth, P. A. Evenson, O. Fadiran, A. R. Fazely, T. Feser, K. Filimonov, B. D. Fox, T. K. Gaisser, J. Gallagher, R. Ganugapati, H. Geenen, L. Gerhardt, A. Goldschmidt, J. A. Goodman, R. Gozzini, S. Grullon, A. Groß, R. M. Gunasingha, M. Gurtneer, A. Hallgren, F. Halzen, K. Han, K. Hanson, D. Hardtke, R. Hardtke, T. Harenberg, J. E. Hart, T. Hauschildt, D. Hays, J. Heise, K. Helbing, M. Hellwig, P. Herquet, G. C. Hill, J. Hodges, K. D. Hoffman, B. Hommez, K. Hoshina, D. Hubert, B. Hughey, P. O. Hulth, K. Hultqvist, S. Hundertmark, J. P. H¨ulß, A. Ishihara, J. Jacobsen, G. S. Japaridze, A. Jones, J. M. Joseph, K. H. Kampert, A. Karle, H. Kawai, J. L. Kelley, M. Kestel, N. Kitamura, S. R. Klein, S. Klepser, G. Kohnen, H. Kolanoski, L. K¨opke, M. Krasberg, K. Kuehn, H. Landsman, H. Leich, I. Liubarsky, J. Lundberg, J. Madsen, K. Mase, H. S. Matis, T. McCauley, C. P. McParland, A. Meli, T. Messarius, P. M´esz´aros, H. Miyamoto, A. Mokhtarani, T. Montaruli, A. Morey, R. Morse, S. M. Movit, K. M¨unich, R. Nahnhauer, J. W. Nam, P. Nießen, D. R. Nygren, H. Ogelman,¨ Ph. Olbrechts, A. Olivas, S. Patton, C. Pe˜na Garay, C. P´erez de Los Heros, A. Piegsa, D. Pieloth, A. C. Pohl, R. Porrata, J. Pretz, P. B. Price, G. T. Przybylski, K. Rawlins, S. Razzaque, F. Refflinghaus, E. Resconi, W. Rhode, M. Ribordy, A. Rizzo, S. Robbins, P. Roth, C. Rott, D. Rutledge, D. Ryckbosch, H. G. Sander, S. Sarkar, S. Schlenstedt, T. Schmidt, D. Schneider, D. Seckel, S. H. Seo, S. Seunarine, A. Silvestri, A. J. Smith, M. Solarz, C. Song, J. E. Sopher, G. M. Spiczak, C. Spiering, M. Stamatikos, T. Stanev, P. Steffen, T. Stezelberger, R. G. Stokstad, M. C. Stoufer, S. Stoyanov, E. A. Strahler, T. Straszheim, K. H. Sulanke, G. W. Sullivan, T. J. Sumner, I. Taboada, O. Tarasova, A. Tepe, L. Thollander, S. Tilav, P. A. Toale, D. Turˇcan, N. van Eijndhoven, J. Vandenbroucke, A. van Overloop, B. Voigt, W. Wagner, C. Walck, H. Waldmann, M.
xi Walter, Y. R. Wang, C. Wendt, C. H. Wiebusch, G. Wikstr¨om, D. R. Williams, R. Wischnewski, H. Wissing, K. Woschnagg, X. W. Xu, G. Yodh, S. Yoshida, and J. D. Zornoza, “First year performance of the IceCube neutrino telescope”, Astroparticle Physics, 26, 155, (2006).
17. A. Achterberg, M. Ackermann, J. Adams, J. Ahrens, K. Andeen, D. W. Atlee, J. N. Bahcall, X. Bai, B. Baret, M. Bartelt, S. W. Barwick, R. Bay, K. Beattie, T. Becka, J. K. Becker, K. H. Becker, P. Berghaus, D. Berley, E. Bernardini, D. Bertrand, D. Z. Besson, E. Blaufuss, D. J. Boersma, C. Bohm, S. B¨oser, O. Botner, A. Bouchta, O. Bouhali, J. Braun, C. Burgess, T. Burgess, T. Castermans, D. Chirkin, J. Clem, J. Conrad, J. Cooley, D. F. Cowen, M. V. D’Agostino, A. Davour, C. T. Day, C. de Clercq, P. Desiati, T. DeYoung, C. Diaz Velez, J. Dreyer, M. R. Duvoort, W. R. Edwards, R. Ehrlich, P. Ekstro¨om, R. W. Ellsworth, P. A. Evenson, O. Fadiran, A. R. Fazely, T. Feser, K. Filimonov, T. K. Gaisser, J. Gallagher, R. Ganugapati, H. Geenen, L. Gerhardt, A. Goldschmidt, J. A. Goodman, R. Gozini, M. G. Greene, S. Grullon, A. Groß, R. M. Gunasingha, M. Gurtner, A. Hallgren, F. Halzen, K. Han, K. Hanson, D. Hardtke, R. Hardtke, T. Harenberg, J. E. Hart, T. Hauschildt, D. Hays, J. Heise, K. Helbing, M. Hellwig, P. Herquet, G. C. Hill, J. Hodges, K. D. Hoffman, K. Hoshina, D. Hubert, B. Hughey, P. O. Hulth, K. Hultqvist, S. Hundertmark, J. P. H¨ulß, A. Ishihara, J. Jacobsen, G. S. Japaridze, A. Jones, J. M. Joseph, K. H. Kampert, A. Karle, H. Kawai, J. L. Kelley, M. Kestel, S. R. Klein, S. Klepser, G. Kohnen, H. Kolanoski, L. K¨opke, M. Krasberg, K. Kuehn, H. Landsman, R. Lang, H. Leich, M. Leuthold, I. Liubarsky, J. Lundberg, J. Madsen, P. Marciniewski, K. Mase, H. S. Matis, T. McCauley, C. P. McParland, A. Meli, T. Messarius, P. M´esz´aros, Y. Minaeva, P. Mioˇcinovi´c, H. Miyamoto, A. Mokhtarani, T. Montaruli, A. Morey, R. Morse, S. M. Movit, K. M¨unich, R. Nahnhauer, J. W. Nam, T. Neunh¨offer, P. Nießen, D. R. Nygren, H. Ogelman,¨ Ph. Olbrechts, A. Olivas, S. Patton, C. Pe˜na Garay, C. P´erez de Los Heros, A. Piegsa, D. Pieloth, A. C. Pohl, R. Porrata, J. Pretz, P. B. Price, G. T. Przybylski, K. Rawlins, S. Razzaque, F. Refflinghaus, E. Resconi, W. Rhode, M. Ribordy, A. Rizzo, S. Robbins, J. Rodr´ıGuez Martino, C. Rott, D. Rutledge, H. G. Sander, S. Schlenstedt, D. Schneider, R. Schwarz, D. Seckel, S. H. Seo, S. Seunarine, A. Silvestri, A. J. Smith, M. Solarz, C. Song, J. E. Sopher, G. M. Spiczak, C. Spiering, M. Stamatikos, T. Stanev, P. Steffen, D. Steele, T. Stezelberger, R. G. Stokstad, M. C. Stoufer, S. Stoyanov, E. A. Strahler, K. H. Sulanke, G. W. Sullivan, T. J. Sumner, I. Taboada, O. Tarasova, A. Tepe, L. Thollander, S. Tilav, P. A. Toale, D. Turˇcan, N. van Eijndhoven, J. Vandenbroucke, B. Voigt, W. Wagner, C. Walck, H. Waldmann, M. Walter, Y. R. Wang, C. Wendt, C. H. Wiebusch, G. Wikstr¨om, D. R. Williams, R. Wischnewski, H. Wissing, K. Woschnagg, X. W. Xu, G. Yodh, S. Yoshida, and J. D. Zornoza, “Limits on the muon flux from neutralino annihilations at the center of the Earth with AMANDA”, Astroparticle Physics, 26, 129, (2006).
xii 18. M. Ackermann, J. Ahrens, H. Albrecht, D. W. Atlee, X. Bai, R. Bay, M. Bartelt, S. W. Barwick, T. Becka, K. H. Becker, J. K. Becker, E. Bernardini, D. Bertrand, D. J. Boersma, S. B¨oser, O. Botner, A. Bouchta, O. Bouhali, J. Braun, C. Burgess, T. Burgess, T. Castermans, D. Chirkin, J. A. Coarasa, B. Collin, J. Conrad, J. Cooley, D. F. Cowen, A. Davour, C. de Clercq, T. De Young, P. Desiati, P. Ekstr¨om T. Feser, T. K. Gaisser, R. Ganugapati, H. Geenen, L. Gerhardt, A. Goldschmidt, A. Groß, A. Hallgren, F. Halzen, K. Hanson, D. Hardtke, R. Hardtke, T. Harenberg, T. Hauschildt, K. Helbing, M. Hellwig, P. Herquet, G. C. Hill, J. Hodges, D. Hubert, B. Hughey, P. O. Hulth, K. Hultqvist, S. Hundertmark, J. Jacobsen, K. H. Kampert, A. Karle, J. Kelley, M. Kestel, L. K¨opke, M. Kowalski, M. Krasberg, K. Kuehn, H. Leich, M. Leuthold, J. Lundberg, J. Madsen, K. Mandlim, P. Marciniewski, H. S. Matis, C. P. McParland, T. Messarius, Y. Minaeva, P. Mioˇcinovi´c, R. Morse, K. M¨unich, R. Nahnhauer, J. W. Nam, T. Neunh¨offer, P. Niessen, D. R. Nygren, H. Ogelman,¨ P. Olbrechts, C. P´erez de Los Heros, A. C. Pohl, R. Porrata, P. B. Price, G. T. Przybylski, K. Rawlins, E. Resconi, W. Rhode, M. Ribordy, S. Richter, J. Rodr´ıguez Martino, H. G. Sander, K. Schinarakis, S. Schlenstedt, D. Schneider, R. Schwarz, S. H. Seo, A. Silvestri, M. Solarz, G. M. Spiczak, C. Spiering, M. Stamatikos, D. Steele, P. Steffen, R. G. Stokstad, K. H. Sulanke, I. Taboada, O. Tarasova, L. Thollander, S. Tilav, J. Vandenbroucke, L. C. Voicu, W. Wagner, C. Walck, M. Walter, Y. R. Wang, C. H. Wiebusch, R. Wischnewski, H. Wissing, K. Woschnagg, and G. Yodh, “New results from the Antarctic Muon and Neu trino Detector Array”, Nuclear Physics B Proceedings Supplements, 143, 343, (2005).
19. M. Ackermann, J. Ahrens, H. Albrecht, D. Atlee, X. Bai, R. Bay, M. Bartelt, S. W. Barwick, T. Becka, K. H. Becker, J. K. Becker, E. Bernardini D. Bertrand, D. J. Boersma, S. B¨oser, O. Botner, A. Bouchta, O. Bouhali, J. Braun, C. Burgess, T. Burgess, T. Castermans, D. Chirkin, T. Coarasa, B. Collin, J. Conrad, J. Cooley, D. F. Cowen, A. Davour, C. de Clercq, T. DeYoung, P. Desiati, P. Ekstr¨om, T. Feser, T. K. Gaisser, R. Ganugapati, H. Geenen, L. Gerhardt, A. Goldschmidt, A. Groß, A. Hallgren, F. Halzen, K. Hanson, D. Hardtke, R. Hardtke, T. Harenberg, T. Hauschildt, K. Helbing, M. Hellwig, P. Herquet, G. C. Hill, J. Hodges, D. Hubert, B. Hughey, P. O. Hulth, K. Hultqvist, S. Hundertmark, J. Jacobsen, K. H. Kampert, A. Karle, J. Kelley, M. Kestel, L. K¨opke, M. Kowalski, M. Krasberg, K. Kuehn, H. Leich, M. Leuthold, J. Lundberg, J. Madsen, K. Mandli, P. Marciniewski, H. S. Matis, C. P. McParland, T. Messarius, Y. Minaeva, P. Mioˇcinovi´c, R. Morse, S. Movit, K. M¨unich, R. Nahnhauer, J. W. Nam, T. Neunh¨offer, P. Niessen, D. R. Nygren, H. Ogelman,¨ P. Olbrechts, C. P´erez de Los Heros, A. C. Pohl, R. Porrata, P. B. Price, G. T. Przybylski, K. Rawlins, E. Resconi, W. Rhode, M. Ribordy, S. Richter, J. Rodr´ıguez Martino, D. Rutledge, H. G. Sander, K. Schinarakis, S. Schlenstedt, D. Schneider, R. Schwarz, A. Silvestri, M. Solarz, G. M. Spiczak, C. Spiering, M. Stamatikos, D. Steele, P. Steffen, R. G. Stokstad, K. H. Sulanke, I. Taboada, O. Tarasova, L. Thollander, S. Tilav, L. C. Voicu, W. Wagner, C.
xiii Walck, M. Walter, Y. R. Wang, C. H. Wiebusch, R. Wischnewski, H. Wissing, K. Woschnagg, and G. Yodh, “Flux limits on ultra high energy neutrinos with AMANDA B10”, Astroparticle Physics, 22, 339, (2005).
20. P. Desiati, A. Achterberg, M. Ackermann, J. Ahrens, H. Albrecht, D. W. Atlee, J. N. Bahcall, X. Bai, M. Bartelt, R. Bay, S. W. Barwick, T. Becka, K. H. Becker, J. K. Becker, P. Berghaus, J. Bergmans, D. Berley, E. Bernardini, D. Bertrand, D. Z. Besson, E. Blaufuss, D. J. Boersma, C. Bohm, S. B¨oser, O. Botner, A. Bouchta, O. Bouhali, J. Braun, C. Burgess, T. Burgess, W. Carithers, T. Castermans, J. Cavin, W. Chinowsky, D. Chirkin, J. Clem, J. A. Coarasa, B. Collin, J. Conrad, J. Cooley, D. F. Cowen, A. Davour, C. T. Day, C. de Clercq, T. DeYoung, W. R. Edwards, R. Ehrlich, P. Ekstr¨om, R. W. Ellsworth, P. A. Evenson, A. Fazely, T. Feser, T. K. Gaisser, J. Gallagher, R. Ganugapati, H. Geenen, L. Gerhardt, A. Goldschmidt, A. Gross, R. M. Gunasingha, A. Hallgren, F. Halzen, K. Hanson, D. Hardtke, R. Hardtke, T. Harenberg, T. Hauschildt, D. Hays, K. Helbing, M. Hellwig, P. Herquet, G. C. Hill, J. Hodges, K. Hoshina, D. Hubert, B. Hughey, P. O. Hulth, K. Hultqvist, S. Hundertmark, J. Jacobsen, G. S. Japaridze, A. Jones, J. M. Joseph, K. H. Kampert, A. Karle, H. Kawai, J. Kelley, M. Kestel, N. Kitamura, S. W. Klein, R. Koch, G. Kohnen, L. K¨opke, M. Kowalski, M. Krasberg, K. Kuehn, E. Kujawski, N. Langer, H. Leich, M. Leuthold, I. Liubarsky, J. Lundberg, J. Madsen, K. Mandli, P. Marciniewski, H. S. Matis, C. P. McParland, T. Messarius, P. M´esz´aros, Y. Minaeva, R. H. Minor, P. Mioˇcinovi´c, H. Miyamoto, R. Morse, K. M¨unich, R. Nahnhauer, J. W. Nam, T. Neunh¨offer, P. Niessen, D. R. Nygren, H.Ogelman,¨ P. Olbrechts, S. Patton, R. Paulos, C. Pe˜na Garay, C. P´erez de Los Heros, A. C. Pohl, R. Porrata, J. Pretz, P. B. Price, G. T. Przybylski, K. Rawlins, S. Razzaque, E. Resconi, W. Rhode, M. Ribordy, S. Richter, J. Rodr´ıguez Martino, H. G. Sander, K. Schinarakis, S. Schlenstedt, D. Schneider, R. Schwarz, D. Seckel, S. H. Seo, A. Silvestri, A. J. Smith, M. Solarz, J. E. Sopher, G. M. Spiczak, C. Spiering, M. Stamatikos, T. Stanev, D. Steele, P. Steffen, T. Stezelberger, R. G. Stokstad, S. Stoyanov, T. D. Straszheim, K. H. Sulanke, G. W. Sullivan, T. J. Sumner, I. Taboada, O. Tarasova, L. Thollander, S. Tilav, D. Turˇcan, N. van Eijndhoven, J. Vandenbroucke, L. C. Voicu, W. Wagner, C. Walck, M. Walter, Y. R. Wang, C. H. Wiebusch, R. Wischnewski, H. Wissing, K. Woschnagg, J. Yeck, S. Yoshida, and G. Yodh, “Neutrino Astronomy and Cosmic Rays at the South Pole: Latest Results from AMANDA and Perspectives for Icecube”, International Journal of Modern Physics A, 20, 6919, (2005).
FIELDS OF STUDY
Major Field: Astronomy
xiv Table of Contents
Abstract...... ii
Dedication...... iv
Acknowledgments...... v
Vita ...... vi
ListofTables ...... xviii
ListofFigures...... xix
Chapter 1 Introduction ...... 1
1.1 GalaxyFormationandEnvironment...... 2
1.2 Active Galactic Nuclei, Feedback and Galaxy Growth ...... 4
1.3 Evolution ...... 7
1.4 Cosmology...... 10
1.5 Scope ...... 10
Chapter 2 Observations & Data Reduction ...... 12
2.1 ClusterSample ...... 12
2.2 VisiblePhotometry...... 13
2.3 SpitzerReduction...... 15
2.3.1 PhotometricCorrections ...... 17
Chapter 3 Physical Member Properties and Statistical Methods .. 85
xv 3.1 ModelSEDs...... 86
3.1.1 AGNIdentification ...... 87
3.1.2 Bolometric AGN Luminosities ...... 95
3.1.3 StellarMasses...... 97
3.1.4 Star FormationRates...... 100
3.2 Partial Correlation Analysis ...... 102
3.3 CompletenessCorrections ...... 103
3.3.1 SpectroscopicCompleteness ...... 104
3.3.2 Mid InfraredCompleteness...... 107
3.3.3 MergedClusterSample...... 109
3.4 LuminosityFunctions...... 110
3.4.1 TotalInfraredLuminosities ...... 110
3.4.2 Construction ...... 111
Chapter 4 AGN in Low-Redshift Clusters ...... 163
4.1 AGNSample ...... 163
4.1.1 X rayAGNSample...... 164
4.1.2 IRAGNSample...... 166
4.2 X raySensitivity ...... 166
4.3 HostGalaxies ...... 168
4.4 AccretionRates...... 172
4.5 RadialDistributions ...... 175
4.6 Discussion...... 176
Chapter 5 Impact of Cluster Environment on Star Formation .... 192
xvi 5.1 PartialCorrelationResults...... 193
5.2 Mass–RadiusRelation ...... 196
5.3 EnvironmentalDependenceofSFR ...... 199
5.4 TIRLuminosityFunction ...... 201
5.5 SubstructureandPreprocessing ...... 205
5.6 MIRButcher OemlerEffect ...... 206
5.7 Discussion...... 207
5.7.1 StarFormationinClusters...... 208
5.7.2 Evolution ...... 214
Chapter 6 Conclusions & Outlook ...... 225
6.1 AGNandTheirHostGalaxies...... 225
6.2 StarFormationinClusters...... 227
6.3 FutureWork...... 228
Bibliography ...... 230
xvii List of Tables
2.1 ClusterSample ...... 21
2.2 SpitzerObservationsbyCluster ...... 22
2.3 VisibleClusterMemberPhotometry ...... 23
2.4 MIRClusterMemberPhotometry ...... 54
3.1 ClusterMemberSummary ...... 126
3.2 IRAGNSelectionEfficiency ...... 158
3.3 SpectroscopicCompleteness ...... 159
3.4 MIRCompleteness ...... 160
4.1 Identified Active Galactic Nuclei ...... 189
5.1 PartialCorrelationResults ...... 224
xviii List of Figures
3.1 Model SEDs for galaxies hosting M06 X ray point sources ...... 114
3.2 Model SEDs for galaxies hosting X ray point sources ...... 115
3.3 Model SEDs for galaxies hosting X ray point sources ...... 116
3.4 Model SEDs for IR AGN not identified in the X rays ...... 117
3.5 Model SEDs for IR AGN not identified in the X rays ...... 118
3.6 Likelihood ratio (ρ) distributions for fits to artificial galaxies with no AGNcomponent...... 119
3.7 Application of F statistics for AGN identification ...... 120
3.8 Comparison of SFRs determined using IRAC and MIPS separately. . 121
3.9 X ray bolometric corrections determined from the data...... 122
3.10 Comparison of galaxy density with the fraction of spectra that are clustermembersversusradius...... 123
3.11 Spectroscopic completeness to cluster members...... 124
3.12 Mid IR completeness at the positions of cluster members...... 125
4.1 X rayAGNselectiondiagram ...... 181
4.2 Positions of AGN and normal galaxies in the IRAC color color space . 182
4.3 Comparison of stellar mass, SFR and sSFR for AGN hosts and normal clustermembers...... 183
4.4 Visible color magnitude diagrams ...... 184
4.5 Black hole accretion rates and fractional growth relative to the host forIRandX rayAGN ...... 185
xix 4.6 Relationships of black hole accretion rates (M˙ BH ) to stellar masses and sSFRs...... 186
4.7 Relationship between black hole growth and star formation in the host galaxy ...... 187
4.8 Radial distributions of IR and X ray AGN compared to all cluster members...... 188
5.1 Correlations of SFR with stellar mass and environment ...... 216
5.2 Radial distributions of cluster members separated by mass and IR excess217
5.3 Average stellar mass as a function of radius ...... 218
5.4 Averagedstarformationasafunctionofradius ...... 219
5.5 Total infrared (TIR) luminosity functions for each of the 5 clusters in themainsample...... 220
5.6 TIR luminosity function of the stacked cluster sample ...... 221
5.7 TIR luminosity function binned by radius ...... 222
5.8 Fraction of star forming galaxies as a function of redshift ...... 223
xx Chapter 1
Introduction
The current paradigm for the evolution of the universe and the growth of structure is based largely on observations of the luminous matter in the universe, i.e. individual galaxies, groups and clusters, and the cosmic microwave background. While galaxy formation physics can often be neglected, the era of precision cosmology sometimes demands detailed knowledge of galaxy formation to map observations of luminous matter onto the dark matter halos that host them (e.g. van Daalen et al. 2011). To do so precisely, we must understand galaxy evolution and its relationship with environment.
On large scales, galaxy formation depends only on the distribution of dark matter halos, which allows measurements of cosmology with relatively simple techniques like sub halo abundance matching (Conroy et al. 2006; Vale & Ostriker 2006; Simha et al. 2010) and halo occupation distributions (Jing et al. 1998; Zheng et al. 2009). However, these techniques provide at best limited insight into the mechanics of galaxy formation. Models that can predict how galaxies form in individual dark matter halos and explain why galaxies have the observed masses, shapes and colors require a more detailed treatment of baryonic physics. These models generally include feedback from both star formation and active galactic nuclei (AGN) to prevent over cooling of gas (Somerville et al. 2008). The feedback prescriptions allow simulations to reproduce observations of galaxies in the local universe. However, the degree to which the feedback prescriptions resemble the
1 processes that operate in real galaxies remains unclear (Somerville et al. 2008; Hopkins & Elvis 2010). Furthermore, the observed relationships between galaxies and their environments (Dressler 1980; Kauffmann et al. 2004; Wetzel et al. 2011) remain difficult to model. Observations of the relationships between galaxies, AGN and environment can probe the physical processes that govern the connections between these different classes of objects.
1.1. Galaxy Formation and Environment
Galaxy formation theory dates to the middle of the twentieth century. Early work explored the physical processes responsible for star formation (Whipple 1946), speculated about the origins of the Milky Way (Eggen et al. 1962), and examined the impact of environment on galaxy evolution (Spitzer & Baade 1951). Osterbrock (1960) discovered that star forming galaxies (SFGs) are less common in galaxy clusters than in lower density environments, and this result was subsequently confirmed with larger samples (Gisler 1978; Dressler et al. 1985). The dearth of star formation in galaxy clusters is mirrored by an under abundance of spiral galaxies in these high density regions, known as the morphology density relation (Dressler 1980; Postman & Geller 1984; Dressler et al. 1997; Postman et al. 2005).
The impact of environment on the frequency and intensity of star formation has been studied intensely in galaxy clusters and also at a variety of other density scales. These measurements have employed both visible wavelength colors (Kodama & Bower 2001; Balogh et al. 2004; Barkhouse et al. 2009; Hansen et al. 2009) and emission lines (Abraham et al. 1996; Balogh et al. 1997, 2000; Kauffmann et al. 2004; Christlein & Zabludoff 2005; Poggianti et al. 2006; Verdugo et al. 2008; Braglia et al. 2009; von der Linden et al. 2010) as well as mid infrared (MIR) luminosities (Bai
2 et al. 2006; Saintonge et al. 2008; Bai et al. 2009). SFGs are consistently found to be more common and to have higher SFRs in lower density environments.
The observed trend in SFR with environment is usually attributed to changes in the sizes of cold gas reservoirs among galaxies in different density regimes. Several mechanisms have been proposed to deplete galaxies’ cold gas reservoirs, and thus to transform galaxies from star forming to passive. These mechanisms include ram pressure stripping of cold gas (RPS; Gunn & Gott 1972; Abadi et al. 1999; Quilis et al. 2000; Roediger & Hensler 2005; Roediger & Br¨uggen 2006, 2007; J´achym et al. 2007), gas starvation (Larson et al. 1980; Balogh et al. 2000; Bekki et al. 2002; Kawata & Mulchaey 2008; McCarthy et al. 2008; Book & Benson 2010), galaxy harassment (Moore et al. 1996, 1998; Lake et al. 1998), and interactions with the cluster tidal potential (Merritt 1983, 1984; Natarajan et al. 1998). Each mechanism operates on a different characteristic timescale and has its greatest impact on galaxies of different masses and at different radii. Gas starvation operates throughout clusters, and it converts galaxies from star forming to passive on a gas exhaustion timescale, which is 2.5 Gyr for normal spiral galaxies (Bigiel et al. ∼ 2011). This time is similar to the cluster crossing time of 2.4 Gyr, which is the timescale appropriate for dynamical processes like galaxy harassment. This contrasts sharply with the timescale appropriate for RPS, which truncates star formation on a gas stripping timescale, which is of order 105 yr. The efficiency of RPS also scales with ICM density, so it operates much more strongly near cluster centers than either starvation or harassment.
The variation of SFR with environment can probe the relative importance of different environmental processes, but the conclusions sometimes conflict. For example, Moran et al. (2007) identified passive spirals in a sample of z 0.5 ≈ clusters and determined that spiral galaxies rapidly turn passive when they enter
3 the cluster environment and then evolve into S0 galaxies. Bai et al. (2009) argue that the similarity of the 24 m luminosity functions observed in galaxy clusters and in the field suggests that the transition from star formation to quiescence must be rapid, which implies that ram pressure stripping (RPS) is the dominant mechanism. Verdugo et al. (2008) and von der Linden et al. (2010), by contrast, find a significant trend of increasing SFR with radius to at least 2R200 from cluster centers. Because the trend of SFR with radius appears to extend to the virial shock (White & Frenk 1991), von der Linden et al. (2010) conclude that preprocessing at the group scale is important. Patel et al. (2009) find a similar trend for increasing average SFR with decreasing local density down to group scale densities (Σ 1.0 Mpc−2). RPS is gal ≈ inefficient in low density gas, so preprocessing in group environments (Zabludoff & Mulchaey 1998; Fujita 2004) is likely driven by processes like gas starvation that operate in less dense environments. While preprocessing appears to be important in some groups and clusters, Berrier et al. (2009) found that very few cluster galaxies have previously resided in groups, so the impact of preprocessing on a typical cluster galaxy must be minimal.
1.2. Active Galactic Nuclei, Feedback and Galaxy Growth
One of the key discoveries in extragalactic astronomy made during the past decade is the existence of tight correlations between the mass of supermassive black holes (SMBHs) and their host galaxies. These relations include the MBH –σ relation (Ferrarese & Merritt 2000; Gebhardt et al. 2000; G¨ultekin et al. 2009), and the
MBH –Mbulge relation (Magorrian et al. 1998; Marconi & Hunt 2003; H¨aring & Rix 2004). These two relations are intimately related, since σ is driven by the mass
4 and dynamical structure of spheroids, and these two quantities are also strongly correlated with one another (Djorgovski & Davis 1987; Dressler & Shectman 1987). Several groups have used a multitude of techniques to examine the evolution in the scaling relations between black holes and spheroids (Peng et al. 2006; Woo et al. 2008; Bennert et al. 2010). Bennert et al. (2010), for example, examined host galaxies of Seyfert type AGN and found evidence that the MBH –Mbulge relation evolves significantly from z =0 to z 1. They found that the masses of SMBHs in ≈ high z galaxies correlate much more closely with the total masses of their hosts than with the spheroid (or bulge) mass. This suggests that the stellar mass responsible for the black hole scaling relations is largely in place by z = 1, and that this mass is re distributed from disks into spheroids by dynamical processing in the time that passes from z =1 to z = 0.
The tightness of the black hole scaling relations suggests that spheroids must form in closely related processes. If the growth of SMBHs during intense AGN phases somehow regulates star formation in the host galaxy, it would provide an explanation for the close correlation between SMBH and bulge masses. The process wherein the growth of a black hole regulates both its own growth and that of its host galaxy is called AGN Feedback Silk & Rees (1998); Croton et al. (2006); Hopkins et al. (2006, 2008); Treister et al. (2011). AGN feedback can also resolve a key problem exhibited by early models of galaxy formation: In the absence of a feedback mechanism, models of galaxy formation over predict the abundance of massive galaxies at z = 0. These galaxies are also much bluer than observed locally (Bower et al. 2006). However, there is not yet any comprehensive model that explains the detailed processes that drive the relationship between AGN fueling, star formation and the global properties of galaxies simulated in semi analytic models.
Furthermore, Peng (2007) recently suggested that the MBH –Mbulge relation can arise
5 due to mergers of originally uncorrelated black holes and bulges. Jahnke & Macci`o (2011) confirmed the analytic result of Peng (2007) with numerical models that account for star formation and black hole growth. These results render one of the key arguments in favor of AGN feedback suspect.
In the absence of a comprehensive model for AGN feedback, environment could impact the efficiency of the feedback process and alter the development of the black hole scaling relations. In 1.1, I discussed the star formation–density relation, and § I indicated that this relation is generally believed to arise from variations in the amount of cold gas available to galaxies as a function of environment. AGN also consume cold gas to fuel their luminosity, so similar patterns might be expected among SFGs and AGN. Indeed, recent results show that the luminosities and observed types of AGN depend strongly on environment (e.g. Kauffmann et al. 2004; Popesso & Biviano 2006; Constantin et al. 2008; Montero Dorta et al. 2009) for AGN selected via visible wavelength emission line diagnostics. Von der Linden et al. (2010) find fewer “weak AGN” (primarily LINERS) among red sequence galaxies near the centers of clusters compared to the field, but they find no corresponding dependence among blue galaxies. Intriguingly, while Montero Dorta et al. (2009) independently report a decline in the fraction of low luminosity AGN toward the centers of low redshift clusters, they find an increase in the fraction of LINERs in higher density environments. The difference is likely a result of evolution. Montero Dorta et al. (2009) found qualitatively different behavior between their main z 1 sample and the result produced when they applied their analysis to ∼ SDSS clusters. These results indicate that the variation of galaxy properties with local environment may influence the types of AGN observed and that evolution in the relationship between some AGN classes and their host galaxies is important. Understanding the environmental mechanism that transforms star forming galaxies
6 into passive galaxies in clusters and the simultaneous impact this process has on AGN fueling can provide insight into the impact of environment on galaxy evolution more generally and how AGN feeding and feedback depend on the environment of the host galaxy.
The wide variety of AGN selection techniques employed in more recent studies represents an important step forward in understanding the dependence of AGN on environment. Several recent papers have used X rays to study the frequency and distribution of AGN in galaxy clusters (Martini et al. 2006, henceforth M06; Martini et al. 2007; Sivakoff et al. 2008; Arnold et al. 2009; Hart et al. 2009) and their evolution with redshift (Eastman et al. 2007; Martini et al. 2009). Martini et al. (2009) found that the AGN fraction among cluster members increases with decreasing local density and increases dramatically (f (1 + z)5.3±1.7) AGN ∝ with redshift. They also found that X ray identification produces a much larger AGN sample than visible wavelength emission line diagnostics: only 4 of the 35 X ray sources identified as AGN by M06 would be classified as AGN from their visible wavelength emission lines. Similar results have been found when comparing radio, X ray and mid IR AGN selection techniques for field AGN (e.g. Hickox et al. 2009).
1.3. Evolution
In 1.1, I introduced the established relationship between star formation and § environment, and I discussed the related tendency for AGN to be less frequent in clusters compared to the field in 1.2. Over the history of the universe, both § the average SFR and average black hole accretion rate have evolved significantly (Hopkins et al. 2006). This trend also manifests among galaxy clusters, which show
7 substantially larger SFRs at high z (Kauffmann et al. 2004; Poggianti et al. 2006, 2008) than their low z counterparts. In fact, the star formation–density relation appears to reverse by z 2. By this redshift, the average SFR becomes larger in ≈ clusters than in the field (Tran et al. 2010; Hatch et al. 2011). However, even high z cluster galaxies form their stars earlier than coeval field galaxies (Rettura et al. 2011), which is an expression of the so called “downsizing” phenomenon (Cowie et al. 1996).
The relationships between SFR, morphology and environment in the local universe place strong constraints on models for galaxy evolution. Another important factor is the presence of an evolutionary trend for galaxies to have higher SFRs at higher redshifts. This was originally reported as an excess of blue cluster members at z 0.4 compared to z = 0 (Oemler 1974; Butcher & Oemler 1978, 1984), and is ≈ commonly known at the Butcher Oemler Effect. This trend tracks the increase in the global SFR and spiral galaxy fraction as a function of redshift. It has also been examined in the MIR (Saintonge et al. 2008; Haines et al. 2009; Tran et al. 2010; Hatch et al. 2011), which is sensitive to dust enshrouded star formation.
Furthermore, recent work on the AGN fraction (fAGN ) in clusters has found that fAGN increases dramatically with redshift Eastman et al. (2007); Martini et al. (2009). This mirrors a similar trend among the global fraction of AGN, which closely follows the variation in the global average SFR (Hopkins & Beacom 2006; Soifer et al. 2008; Haines et al. 2009; Martini et al. 2009). The similarity of these trends suggests that some fundamental property connects star formation and black hole accretion. One likely culprit is the cold gas reservoir, since both star formation and AGN consume cold gas as fuel. More broadly, the notion that star formation and AGN must be connected somehow is called “feedback”, which I introduced in 1.2. §
8 The evolution between black holes and their host galaxies depends on redshift (Peng et al. 2006; Woo et al. 2008; Bennert et al. 2010). The variation in this dependence between clusters and the field can probe both the processes that mediate AGN feedback and the origin of observed relationships between galaxies and their environment. However, the epoch of cluster assembly (0 z < 1.5, e.g. Berrier et al. ≤ ∼ 2009) coincides with a rapid decline in the intensity of both star formation (e.g. Madau et al. 1998; Hopkins et al. 2006) and AGN (e.g. Shaver et al. 1996; Boyle & Terlevich 1998; Shankar et al. 2009), which makes it difficult to disentangle rapid environmental effects from the effects in the decline of the global SFR and accretion rates. Dressler & Gunn (1983) found early evidence for an increase in AGN activity with redshift, and the Butcher Oemler effect had already provided evidence for a corresponding increase in SFRs. In the last decade, the proliferation of observations of high redshift galaxy clusters at X ray, visible and infrared wavelengths has yielded measurements of similar trends in the fraction of both AGN (Eastman et al. 2007; Martini et al. 2009) and star forming galaxies (Poggianti et al. 2006, 2008; Saintonge et al. 2008; Haines et al. 2009) identified using a variety of methods. These newer results have also examined cluster members confirmed from spectroscopic redshifts rather than relying solely on statistical excesses in cluster fields, which permits more detailed study of the relationships between galaxies and their parent clusters.
The observed trends of star formation and accretion with redshift, particularly the rates of evolution and any differences between these rates, can probe the processes that lead to the observed deficit of SFGs and AGN in clusters. This is because the proposed mechanisms to introduce the star formation–radius relation operate on different timescales. As a result, the rapidity in the decline of star formation and accretion with time relative to their counterparts in the field constrains the mechanism that removes the fuel supply for these objects. More broadly, these
9 factors are also sensitive to the processes that fuel AGN and the connection between black hole activity and host galaxy growth. For example, the relative change in the fraction of AGN and SFGs compared to the change in the global cold gas reservoir can probe how efficiently the cold ISM in galaxies can be funneled to the galaxy center to fuel an AGN. Mechanisms for AGN fueling, feedback, and the processes that mediate the star formation–density relation represent some of the major uncertainties in galaxy evolution theory, and they are areas of broad interest.
1.4. Cosmology
To translate from observed fluxes to luminosities, which are required to measure intrinsic properties such as SFRs and black hole accretion rates of the galaxies and AGN I examine, I must adopt a choice of cosmology. I will employ the WMAP
5 year cosmology—a Λ CDM universe with m = 0.26, Λ = 0.74 and h = 0.72 (Dunkley et al. 2009)—throughout this dissertation. The uncertainties introduced by this choice are a few percent, which is dwarfed by the statistical and systematic uncertainties introduced in my analysis. For example, the translation of observed luminosities to stellar masses has systematic uncertainties of approximately 0.3 dex. I will therefore neglect the uncertainty associated with the choice of cosmology throughout the rest of the text.
1.5. Scope
In this dissertation, I develop observational constraints on the fueling of low luminosity AGN and on the processes responsible for the star formation–density relation. AGN feedback is not expected to operate efficiently in low luminosity
10 AGN, so the low luminosity AGN characteristic of the cluster environment are poor sources to measure AGN feedback. Therefore, my analysis will not directly address the question of AGN feedback, but the constraints I can place on the environmental processes that impact both AGN and star formation may indirectly provide insight into the operation of AGN feedback. To accomplish these goals, I extend and expand upon the work of Martini et al. (2006, 2007, 2009) by supplementing their X ray imaging and visible wavelength photometry with MIR observations from the Spitzer Space Telescope. I use these data to select AGN independent of their X ray emission. I also measure the stellar masses and SFRs of cluster member galaxies from fits to their visible to MIR SEDs.
The document is organized as follows: I discuss the data reduction and photometry of the visible and MIR observations Chapter 2. Chapter 3 details the techniques I employ to identify AGN, to measure properties of cluster member galaxies and to construct statistically complete samples. I present the properties of the AGN and their host galaxies found in the cluster sample in Chapter 4, and I examine the properties of star forming galaxies (SFGs) in clusters and what these properties indicate about the influence of the cluster environment on galaxies’ cold gas reservoirs in Chapter 5. Finally, in Chapter 6 I summarize my results and briefly discuss avenues for additional work.
11 Chapter 2
Observations & Data Reduction
In this chapter I will summarize the observations employed in my analysis, and I will discuss the procedures used to convert the raw data to a format suitable for analysis. The observations that form the input sample I employ in my analysis consist of both visible ( 2.2) and MIR imaging ( 2.3). I will discuss the corrections § § for Galactic extinction and for instrumental effects in Section 2.3.1. I also employ redshifts from the literature to determine cluster membership. These redshifts were taken from a variety of sources with different selection and completeness functions, which in turn necessitates careful correction for the effective completeness of the redshifts in the literature. I will take up the subject of completeness corrections in Chapter 3. First, however, I will introduce the sample of clusters employed in my analysis ( 2.1). §
2.1. Cluster Sample
I employ 8 low z galaxy clusters as my input sample. These clusters are Abell 3125 (A3125), A3128, A644, A1689, A2163, MS 1008.1 1224 (MS1008) and AC114. These clusters all have X ray observations in the Chandra archive, and Paul Martini and collaborators proposed MIR observations with the Spitzer Space Telescope for these clusters. The Spitzer observations targeted fields around X ray point sources to
12 allow examination of these sources in the MIR and measurements of star formation in their host galaxies.
To determine cluster membership of galaxies in the cluster field, I employ redshifts reported in Martini et al. (2007) or extracted from the NASA Extragalactic Database1. I consider a galaxy to be a cluster member if it satisfies the 3σ redshift ± interval established by Martini et al. (2007) and if it falls within a circular field with radius,
σ R < R =1.7h−1 Mpc [(1 + z)3 + ]−1/2 (2.1) 200 1000 km s−1 m Λ where σ is the cluster’s velocity dispersion (Treu et al. 2003). The velocity dispersions were established using the biweight velocity dispersion estimator of Beers et al. (1990). These criteria yield a sample of 1165 cluster member galaxies. I eliminate many of these galaxies from the sample due to either limited photometric coverage or, in a few instances, because the spectroscopic redshifts in the literature are in clear disagreement with the photometric redshifts obtained from the SED fits (Section 3.1). The final sample of “good” cluster members, those galaxies with detections in at least 5 bands and with apparently reliable spectroscopic redshifts, contains 488 galaxies.
2.2. Visible Photometry
Visible wavelength images of the clusters in the sample clusters were obtained with the 2.5m du Pont telescope at Las Campanas by M06. I provide a brief summary of the processing of these data. The reader is referred to M06 for the full details.
1http://nedwww.ipac.caltech.edu/
13 All 8 clusters in the sample have B , V and R band imaging, and 4 of the 8 have I band imaging. I extracted separate source catalogs for each of these bands using Source Extractor (SExtractor, Bertin & Arnouts 1996) and merged the catalogs using the R band image as the reference image for astrometry and total (Kron) magnitudes. I correct from aperture to total magnitudes at constant color by applying the R band aperture corrections to the aperture magnitudes determined in all bands,
m = m (R R ) (2.2) Kron Ap − Ap − Kron where mAp and mKron are the aperture and Kron like magnitudes, respectively, for the band being corrected. Rather than taking the published photometry from M06, I use redshift dependent apertures assigned individually to each cluster. These apertures approximate a fixed metric aperture with radii that correspond to 10 kpc at the redshift of each cluster. These large apertures yield relatively small aperture corrections, typically 0.1 mag. ∼
SExtractor returns R band positions that are good to within a fraction of an arcsecond. However, the positions of sources in IRAC and MIPS images are less precise due to the poorer angular resolution and larger pixel sizes in these bands. I select the best astrometric matches to each Spitzer source from the objects identified by SExtractor within a specified search radius, θ. To determine the best value of θ, I scrambled the RA of SExtractor sources and determined how many Spitzer sources were matched to a scrambled galaxy as a function of θ. I find the best balance between purity and completeness for θ 1′′.25. This search radius yields spurious ≈ matches for less than 2% of objects. The actual fraction of mismatched sources in the catalog will be much lower, because a Spitzer object with a spurious match will usually be better matched to its “true” counterpart, which has a median match distance d = 0′′.4. The images used to perform the matching do not suffer from
14 substantial confusion, even in the cluster centers, so erroneous photometry due to overlapping sources is unlikely to present a problem.
2.3. Spitzer Reduction
I processed mid infrared (MIR) observations from the Spitzer Space Telescope using the IRAC (λeff = 3.6, 4.5, 5.8, 8.0 m; Fazio et al. 2004) and MIPS
(λeff = 24; Rieke et al. 2004) instruments from Spitzer program 50096 (P.I. Martini). Observations were carried out between 2008 November 1 and 2009 April 22. Spitzer pointings were chosen to image the X ray point sources in 8 low redshift galaxy clusters examined by M06. I supplemented these observations with data from the Spitzer archive for Abell 1689 and AC 114.
Spitzer’s cryogen ran out before the MIPS observations of three clusters (Abell 644, Abell 1689 and MS 1008.1 1224) were carried out. In one of these clusters (Abell 1689), I extended my coverage to 24 m using observations from the Spitzer archive, leaving two clusters with no usable MIPS observations. The clusters that make up the sample are summarized in Table 2.1, which includes approximate observer frame luminosity limits for the 8 m and 24 m mosaics of each cluster. These limits are approximate because the image depth varies across the mosaics as the number of overlapping pointings changes. Quoted limits correspond to areas with “full coverage”–all the frames from a given pointing cover that pixel–but without overlap from adjacent pointings. The Astronomical Observation Request (AOR) numbers used to construct the MIR mosaics are listed in Table 2.2.
The raw Spitzer data are reduced by an automated pipeline before they are delivered to the user, but artifacts inevitably remain in the calibrated (BCD)
15 images. Preliminary artifact mitigation for the IRAC images was performed using the IRAC artifact mitigation tool by Sean Carey2. I inspected each corrected image after this step and determined whether the image was immediately usable, if additional corrections were required, or if it simply had too many remaining artifacts to be reliably corrected. The latter class primarily included images with extremely bright stars that caused artifacts so severe that the image became useless. Where appropriate, additional corrections were applied using the muxstripe3 and jailbar4 correctors by Jason Surace and the column pull down corrector5 by Leonidas Moustakas. Artifacts in the MIPS images were removed by applying a flatfield correction algorithm packaged with the Spitzer mosaic software, (MOPEX6), as described on the Spitzer Science Center (SSC) website7.
Image mosaics for IRAC and MIPS were constructed from the artifact corrected images using MOPEX. Aperture photometry was extracted from the resulting mosaics using the apphot package in IRAF. I used the same redshift dependent apertures described in 2.2, which maintain consistent flux ratios across the § wavelength range used to fit SEDs to the observed cluster members. I converted the measured fluxes to magnitudes in the Vega system after the photometric corrections described in Section 2.3.1 had been applied. All magnitudes quoted in this work, both visible and MIR, are calculated in the Vega system. The large apertures associated with a fixed 10 kpc size at low redshift yielded reduced S/N due to an increase in the background contribution. However, most cluster members were sufficiently bright that the uncertainties on the measured fluxes were dominated
2http://spider.ipac.caltech.edu/staff/carey/irac artifacts/ 3http://ssc.spitzer.caltech.edu/dataanalysistools/tools/contributed/irac/automuxstripe/ 4http://ssc.spitzer.caltech.edu/dataanalysistools/tools/contributed/irac/jailbar/ 5http://ssc.spitzer.caltech.edu/dataanalysistools/tools/contributed/irac/cpc/ 6http://ssc.spitzer.caltech.edu/dataanalysistools/tools/mopex/ 7http://ssc.spitzer.caltech.edu/dataanalysistools/cookbook/23/# Toc256425880
16 by systematic errors (5%) in the zero point calibration, except at 24 m. The use of large photometric apertures also allowed galaxies to be treated as point sources for the purpose of computing aperture corrections, as recommended by the SSC. A smaller aperture could improve the S/N, but this gain would be outweighed by the systematic uncertainty introduced by the aperture corrections for the resulting flux measurements, as aperture corrections for moderately extended IRAC sources remain highly uncertain (IRAC Instrument Handbook8).
2.3.1. Photometric Corrections
I estimate the Galactic extinction toward each of the 8 clusters in the sample from the dust map of Schlegel et al. (1998) and calculated extinction corrections assuming RV = 3.1 and the Cardelli et al. (1989) reddening law. The resolution of the Schlegel et al. (1998) dust map necessitates a common extinction correction for all cluster members. However, Galactic cirrus is apparent in some of our images, so this assumption is not always appropriate. This leads to additional uncertainty associated with the extinction corrections, but the total (visual) extinction toward our clusters is typically less than 0.1 mag. The associated uncertainties are therefore small. For the clusters with the highest extinctions (Abell 2104 and 2163, with
AV =0.73 and 1.1, respectively), variations in extinction across the cluster represent an important source of systematic uncertainty. I account for this by adopting a 10% uncertainty in all extinction corrections and propagating this uncertainty to the corrected magnitudes. In Abell 2163, for example, this yields an uncertainty of 0.11 mags in the de reddened V band magnitude.
8http://ssc.spitzer.caltech.edu/irac/iracinstrumenthandbook/IRAC Instrument Handbook.pdf
17 The raw fluxes measured from the MIR mosaics must be corrected for various instrumental effects, including aperture size, IRAC array location, and color, as described in the IRAC and MIPS9 Instrument Handbooks. Aperture corrections are, in principle, required for all observations. In practice, even the smallest apertures I use ( 7′′) are large enough that aperture corrections to visible wavelength ∼ point sources are negligible. For MIR point sources, this is not the case. I apply aperture corrections from the IRAC Instrument Handbook appropriate for our redshift dependent photometric apertures to the IRAC photometry. These corrections are not strictly appropriate due to the extended nature of our sources; however, I have chosen apertures that are large compared to the sources ( 3 ∼ × larger than the FWHM of the largest galaxies, see Section 2.3). I therefore apply aperture corrections appropriate for point sources.
I determined aperture corrections appropriate for the MIPS images by averaging a theoretical point source response function (PRF) from STinyTim10 with three bright, isolated point sources in the Abell 3125 and Abell 2104 mosaics. The PRFs of sources from the different clusters agree with one another and with the theoretical PRF to within a few percent over the range of aperture sizes relevant for our MIPS photometry. The dispersion between the individual PRFs at fixed aperture size provides an estimate of the uncertainty on the corrections and is included in the 24 m error budget. The MIPS images of the other clusters lack bright, isolated points sources with which to make a similar measurement, so I assume that the PRF appropriate for Abell 3125 and Abell 2104 gives reasonable aperture corrections for all clusters. This introduces some systematic error in our derived 24 m fluxes, but
9http://ssc.spitzer.caltech.edu/mips/mipsinstrumenthandbook/MIPS Instrument Handbook.pdf 10http://ssc.spitzer.caltech.edu/dataanalysistools/tools/contributed/general/stinytim/
18 the agreement of the observed PRFs of point sources identified in Abell 3125 and Abell 2104 with the theoretical PRF indicates that this uncertainty is small.
The flatfield corrections applied to IRAC images by the automated image reduction pipeline are based on observations of the zodiacal background light, which is uniform on the scale of the IRAC field of view. It is also extremely red compared to any normal astrophysical source, which alters the internal scattering introduced by the instrument in the flatfield compared to a flat illuminated with a bluer source. This causes the effective bandpass of a “flatfielded” image to vary with position on the detector. Corrections for this effect are provided by SSC in the form of standard array location correction images for each of the IRAC bands. These correction images are intended to be applied directly to a single IRAC image. However, the required corrections are much smaller for mosaiced images because the flux of a given source is averaged over several array positions. The residual effect can be a few percent or more depending on the number of overlapping IRAC pointings, so I construct a array location correction mosaics by co adding the correction image for a single IRAC pointing shifted to the positions of each dithered image in the science mosaic. I measure the required array location corrections in the same apertures used to measure the IRAC fluxes and average over the size of the aperture.
The Spitzer image reduction pipeline assumes a flat power law SED to convert electrons to incident fluxes. Astrophysical sources typically do not show flat SEDs and therefore require color corrections to determine the true flux at the effective wavelength of a given band. This is especially important in star forming galaxies, which show strong polycyclic aromatic hydrocarbon (PAH) emission features at 6.2 and 7.7 m (Smith et al. 2007). I determine color corrections to the measured fluxes from model SEDs ( 3.1). I fit model SEDs to the measured fluxes after all § other corrections have been applied. The fitting procedure is independent of color
19 correction because it averages over the bandpasses rather than measuring the flux at the effective wavelength. Therefore, I can simply integrate the model SED across the various MIR bandpasses and determine the appropriate color corrections following the procedures outlined in the instrument handbooks. The color correction, K, applied to an IRAC source is given by,
−1 (Fν/Fν0 )(ν/ν0) Rν dν K = −2 (2.3) (ν/ν0) Rν dν where Fν is the model spectrum and Rν is the response function of the detector in the appropriate channel. The formalism for MIPS color corrections is similar but slightly more complicated; Interested readers should consult Section 3.7.4 of the MIPS Instrument Handbook. Optical and MIR photometry for each cluster member after all relevant corrections have been applied are listed in Tables 2.3 and 2.4.
20 Cluster z σv Nmembers νLν,obs(8 m) Limit νLν,obs(24 m) Limit (km s−1) (1042 erg s−1) (1041 erg s−1) (1) (2) (3) (4) (5)
Abell3128 0.0595 906 83 0.54 2.6 Abell3125 0.0616 475 25 0.58 2.6 Abell 644 0.0701 952 9 1.0 — Abell2104 0.1544 1242 74 1.2 1.9 Abell1689 0.1867 1400 160 1.3 2.8 Abell2163 0.2007 1381 27 1.8 3.4 MS1008.1 1224 0.3068 1127 68 0.81 — AC114 0.3148 1388 159 1.0 2.2 21
Note. — Summary of clusters included in the analysis and the observations contributing to the MIR mosaic images of each cluster. The extra line beneath Abell 1689 contains additional AORs that do not fit on a single line. (1) Redshifts from Martini et al. (2007), determined using the biweight estimator of Beers et al. (1990). (2) Velocity dispersions of cluster members estimated by Martini et al. (2009) using the biweight measure of Beers et al. (1990). (3) Total number of galaxies with both MIR and R band coverage identified as cluster members by Martini et al. (2007) or extracted from the literature using their redshift limits. (4) The minimum detectable observer frame 8 m luminosity in each cluster, derived from the 3σ lower limit on measurable flux in a “typical” part of the 8 m mosaic image. Due to the variable coverage across the cluster, lower luminosites are detectable in some cluster members than in others. (5) 3σ lower limits on detectable 24 m luminosities. These are derived in a similar manner to the IRAC limits in column (4) and have the same caveats.
Table 2.1. Cluster Sample Cluster IRAC AOR(s) MIPS AOR(s) (1) (2)
Abell3128 25410816 25411072 Abell3125 25409792 25410048 Abell644 25409280 — Abell2104 25411328 25411584 Abell1689 4754176,14696192,14696448 4770048,4769792 14696704,14696960,14697216 19042304 14697472,25411840 19042048 Abell2163 25412352 25412608
22 MS1008.1 1224 25410304 — AC114 4756480,12653824 4773888,4774144 25412864 25413120
Note. — Summary of clusters included in the analysis and the observations contributing to the MIR mosaic images of each cluster. The extra line beneath Abell 1689 contains additional AORs that do not fit on a single line. (1) Astronomical Observation Request (AOR) numbers of Spitzer observations used to contruct IRAC mosaics. (2) AORs used to contruct the 24 m mosaics.
Table 2.2. Spitzer Observations by Cluster Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
a3128 001 03:30:37.7 52:32:57 17.88 0.08 16.81 0.06 16.21 0.06 — ± ± ± a3128 002 03:30:29.7 52:32:54 16.91 0.08 15.89 0.06 15.31 0.06 — ± ± ± a3128 003 03:30:28.5 52:33:53 19.89 0.09 18.98 0.06 18.42 0.06 — ± ± ± a3128 004 03:30:39.3 52:32:05 18.11 0.09 17.01 0.06 16.35 0.06 — ± ± ± a3128 005 03:30:42.8 52:32:06 18.44 0.09 17.48 0.06 16.89 0.06 — ± ± ± a3128 006 03:30:30.2 52:31:38 18.73 0.09 17.79 0.06 17.21 0.06 — ± ± ± a3128 007 03:30:41.5 52:34:34 19.75 0.09 18.70 0.06 18.09 0.06 — ± ± ± a3128 008 03:30:22.4 52:34:23 17.07 0.08 16.06 0.06 15.46 0.06 — ± ± ±
23 a3128 009 03:30:47.3 52:34:17 17.95 0.08 16.86 0.06 16.25 0.06 — ± ± ± a3128 010 03:30:50.0 52:34:36 18.34 0.09 17.94 0.06 17.74 0.06 — ± ± ± a3128 011 03:30:25.4 52:30:48 21.45 0.13 20.58 0.09 19.78 0.07 — ± ± ± a3128 012 03:30:17.3 52:34:08 18.27 0.09 17.29 0.06 16.71 0.06 — ± ± ± a3128 013 03:30:21.5 52:31:11 18.00 0.08 17.06 0.06 16.49 0.06 — ± ± ± a3128 014 03:30:15.8 52:33:50 18.62 0.09 17.63 0.06 17.01 0.06 — ± ± ± a3128 015 03:30:19.1 52:35:06 20.43 0.10 19.57 0.07 18.96 0.06 — ± ± ± a3128 016 03:30:15.2 52:34:12 16.77 0.08 15.70 0.06 15.08 0.06 — ± ± ± a3128 017 03:30:38.0 52:36:17 17.42 0.08 16.36 0.06 15.76 0.06 — ± ± ± a3128 018 03:30:19.3 52:31:05 17.27 0.08 16.79 0.06 16.45 0.06 — ± ± ± a3128 019 03:30:16.4 52:31:32 17.39 0.08 16.32 0.06 15.70 0.06 — ± ± ± (continued) Table 2.3. Visible Cluster Member Photometry Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
a3128 020 03:30:55.7 52:33:47 17.39 0.08 16.53 0.06 16.00 0.06 — ± ± ± a3128 021 03:30:13.4 52:33:48 18.96 0.09 17.96 0.06 17.35 0.06 — ± ± ± a3128 023 03:30:16.4 52:35:16 — 20.78 0.12 19.64 0.07 — ± ± a3128 024 03:30:22.2 52:36:17 19.49 0.09 18.47 0.06 17.92 0.06 — ± ± ± a3128 025 03:30:51.0 52:30:31 15.78 0.08 14.65 0.06 13.97 0.06 — ± ± ± a3128 026 03:30:09.4 52:33:30 18.47 0.09 17.39 0.06 16.77 0.06 — ± ± ± a3128 027 03:30:09.5 52:34:09 18.93 0.09 17.90 0.06 17.26 0.06 — ± ± ± 24 a3128 028 03:30:48.4 52:36:35 17.94 0.09 17.00 0.06 16.42 0.06 — ± ± ± a3128 029 03:30:38.4 52:37:10 15.54 0.08 14.41 0.06 13.77 0.06 — ± ± ± a3128 032 03:30:38.0 52:29:03 18.43 0.09 17.36 0.06 16.74 0.06 — ± ± ± a3128 033 03:30:50.2 52:36:42 16.54 0.08 15.49 0.06 14.88 0.06 — ± ± ± a3128 034 03:30:10.2 52:30:57 18.66 0.09 17.77 0.06 17.15 0.06 — ± ± ± a3128 035 03:30:24.0 52:28:42 17.58 0.08 16.71 0.06 16.16 0.06 — ± ± ± a3128 036 03:30:03.0 52:33:06 21.15 0.16 20.30 0.09 19.84 0.07 — ± ± ± a3128 037 03:30:18.6 52:28:55 15.97 0.08 14.89 0.06 14.23 0.06 — ± ± ± a3128 038 03:30:54.9 52:29:19 17.62 0.08 16.70 0.06 16.07 0.06 — ± ± ± a3128 039 03:30:01.7 52:32:20 19.34 0.09 18.32 0.06 17.73 0.06 — ± ± ± a3128 040 03:30:53.9 52:28:56 18.05 0.09 17.08 0.06 16.45 0.06 — ± ± ± a3128 041 03:31:06.0 52:31:03 18.62 0.09 17.59 0.06 16.97 0.06 — ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
a3128 042 03:30:37.0 52:27:59 17.51 0.08 16.41 0.06 15.76 0.06 — ± ± ± a3128 043 03:31:02.5 52:30:04 18.10 0.09 17.47 0.06 17.01 0.06 — ± ± ± a3128 044 03:30:13.7 52:37:30 16.19 0.08 15.04 0.06 14.38 0.06 — ± ± ± a3128 045 03:30:02.1 52:30:56 20.67 0.10 19.68 0.07 19.09 0.06 — ± ± ± a3128 046 03:31:10.2 52:32:25 18.08 0.09 17.02 0.06 16.41 0.06 — ± ± ± a3128 047 03:31:06.7 52:30:39 17.91 0.08 16.81 0.06 16.16 0.06 — ± ± ± a3128 048 03:29:58.1 52:33:23 19.18 0.09 18.11 0.06 17.47 0.06 — ± ± ± 25 a3128 049 03:30:32.3 52:38:49 17.87 0.08 16.79 0.06 16.18 0.06 — ± ± ± a3128 050 03:29:56.3 52:32:35 17.07 0.08 16.10 0.06 15.49 0.06 — ± ± ± a3128 051 03:29:58.4 52:31:03 20.40 0.10 19.38 0.07 18.82 0.06 — ± ± ± a3128 053 03:30:35.8 52:27:11 21.38 0.13 20.21 0.09 19.75 0.07 — ± ± ± a3128 054 03:30:46.2 52:27:26 23.07 0.42 21.70 0.40 21.05 0.16 — ± ± ± a3128 055 03:30:18.6 52:27:26 21.25 0.19 20.35 0.09 19.68 0.07 — ± ± ± a3128 056 03:30:17.1 52:38:54 18.40 0.09 17.66 0.06 17.10 0.06 — ± ± ± a3128 057 03:30:45.5 52:27:06 16.81 0.08 15.70 0.06 15.03 0.06 — ± ± ± a3128 060 03:29:53.0 52:34:10 22.37 0.25 20.57 0.10 20.05 0.08 — ± ± ± a3128 063 03:29:53.8 52:35:03 16.41 0.08 15.25 0.06 14.58 0.06 — ± ± ± a3128 064 03:30:39.7 52:26:29 18.34 0.09 17.32 0.06 16.71 0.06 — ± ± ± a3128 065 03:29:50.6 52:34:47 15.98 0.08 14.83 0.06 14.16 0.06 — ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
a3128 067 03:29:48.6 52:33:17 19.84 0.09 18.79 0.06 18.22 0.06 — ± ± ± a3128 068 03:30:03.5 52:27:57 21.41 0.14 20.27 0.08 19.62 0.07 — ± ± ± a3128 069 03:30:10.6 52:27:07 18.05 0.09 17.03 0.06 16.44 0.06 — ± ± ± a3128 070 03:29:48.5 52:32:09 21.07 0.13 20.14 0.07 19.64 0.06 — ± ± ± a3128 071 03:29:56.3 52:37:28 18.54 0.09 17.46 0.06 16.84 0.06 — ± ± ± a3128 072 03:29:59.9 52:38:13 18.58 0.09 17.48 0.06 16.86 0.06 — ± ± ± a3128 073 03:30:22.1 52:26:08 17.75 0.08 16.67 0.06 15.99 0.06 — ± ± ± 26 a3128 074 03:29:49.2 52:35:57 20.46 0.10 19.61 0.07 19.00 0.06 — ± ± ± a3128 077 03:29:42.6 52:34:55 19.59 0.09 18.54 0.06 17.93 0.06 — ± ± ± a3128 078 03:29:59.0 52:27:05 20.05 0.09 19.10 0.07 18.56 0.06 — ± ± ± a3128 079 03:30:03.6 52:26:29 18.15 0.09 17.14 0.06 16.54 0.06 — ± ± ± a3128 080 03:29:46.7 52:37:05 18.74 0.09 17.65 0.06 17.04 0.06 — ± ± ± a3128 081 03:30:12.2 52:25:39 19.13 0.09 18.11 0.06 17.50 0.06 — ± ± ± a3128 082 03:29:41.6 52:31:16 18.32 0.09 17.24 0.06 16.59 0.06 — ± ± ± a3128 085 03:30:41.1 52:24:47 18.05 0.09 16.94 0.06 16.30 0.06 — ± ± ± a3128 087 03:30:54.3 52:25:09 16.94 0.08 15.87 0.06 15.22 0.06 — ± ± ± a3128 092 03:29:41.4 52:29:35 18.53 0.09 17.68 0.06 17.12 0.06 — ± ± ± a3128 095 03:29:36.0 52:34:16 18.77 0.09 17.71 0.06 17.09 0.06 — ± ± ± a3128 098 03:30:33.9 52:23:53 18.15 0.09 17.58 0.06 17.20 0.06 — ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
a3128 099 03:30:56.5 52:24:22 16.49 0.08 15.46 0.06 14.81 0.06 — ± ± ± a3128 101 03:29:36.7 52:29:35 18.44 0.09 17.54 0.06 16.93 0.06 — ± ± ± a3128 102 03:31:08.2 52:25:04 17.40 0.08 16.35 0.06 15.73 0.06 — ± ± ± a3128 107 03:29:36.3 52:37:17 19.77 0.09 18.98 0.06 18.52 0.06 — ± ± ± a3128 111 03:31:05.3 52:23:54 20.19 0.10 18.96 0.07 18.43 0.06 — ± ± ± a3128 118 03:29:33.8 52:26:59 17.69 0.08 16.50 0.06 15.86 0.06 — ± ± ± a3125 001 03:27:20.2 53:28:34 17.87 0.12 16.89 0.09 16.29 0.09 — ± ± ± 27 a3125 005 03:27:06.6 53:27:21 18.96 0.12 17.92 0.09 17.29 0.09 — ± ± ± a3125 008 03:27:04.1 53:26:55 19.02 0.13 18.14 0.09 17.57 0.09 — ± ± ± a3125 011 03:27:23.5 53:25:35 17.06 0.12 15.93 0.09 15.29 0.09 — ± ± ± a3125 012 03:27:16.9 53:25:31 21.75 0.19 20.59 0.12 19.90 0.10 — ± ± ± a3125 013 03:27:24.8 53:25:17 16.63 0.12 15.52 0.09 14.87 0.09 — ± ± ± a3125 014 03:27:45.9 53:26:29 17.16 0.12 16.10 0.09 15.46 0.09 — ± ± ± a3125 015 03:27:25.3 53:25:06 18.13 0.12 17.00 0.09 16.32 0.09 — ± ± ± a3125 016 03:27:55.8 53:33:18 20.86 0.14 20.20 0.10 20.01 0.10 — ± ± ± a3125 017 03:27:52.1 53:26:09 16.34 0.12 15.24 0.09 14.58 0.09 — ± ± ± a3125 018 03:27:15.4 53:24:27 20.99 0.15 20.09 0.10 19.50 0.09 — ± ± ± a3125 021 03:27:56.6 53:34:59 19.56 0.13 18.58 0.09 17.95 0.09 — ± ± ± a3125 023 03:27:34.0 53:23:52 18.91 0.12 17.88 0.09 17.26 0.09 — ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
a3125 024 03:27:31.1 53:23:40 18.01 0.12 16.93 0.09 16.21 0.09 — ± ± ± a3125 028 03:26:52.3 53:24:58 21.49 0.20 21.25 0.15 20.51 0.11 — ± ± ± a3125 029 03:27:45.4 53:24:02 18.47 0.12 17.44 0.09 16.84 0.09 — ± ± ± a3125 030 03:27:03.3 53:23:36 20.96 0.16 20.19 0.10 19.61 0.09 — ± ± ± a3125 031 03:27:52.6 53:24:08 16.68 0.12 15.74 0.09 15.13 0.09 — ± ± ± a3125 032 03:26:54.8 53:23:37 19.85 0.13 19.09 0.09 18.54 0.09 — ± ± ± a3125 034 03:26:47.5 53:24:05 18.72 0.12 18.17 0.09 17.72 0.09 — ± ± ± 28 a3125 038 03:26:52.8 53:22:57 19.12 0.13 18.14 0.09 17.56 0.09 — ± ± ± a3125 039 03:27:06.2 53:22:02 21.25 0.20 21.16 0.16 20.69 0.12 — ± ± ± a3125 040 03:26:44.8 53:23:25 19.36 0.13 18.36 0.09 17.79 0.09 — ± ± ± a3125 044 03:27:05.0 53:21:41 17.16 0.12 16.12 0.09 15.45 0.09 — ± ± ± a3125 045 03:27:54.7 53:22:17 16.18 0.12 15.08 0.09 14.43 0.09 — ± ± ± a644 005 08:17:25.8 07:33:42 21.05 0.12 20.08 0.09 19.62 0.09 — ± ± ± a644 011 08:17:39.5 07:33:09 17.29 0.12 16.69 0.09 16.20 0.09 — ± ± ± a644 012 08:17:36.4 07:32:16 20.71 0.12 20.10 0.09 19.62 0.09 — ± ± ± a644 013 08:17:42.7 07:36:17 20.83 0.12 19.91 0.09 19.34 0.09 — ± ± ± a644 017 08:17:30.7 07:31:04 21.23 0.13 20.48 0.09 20.05 0.09 — ± ± ± a644 020 08:17:32.4 07:30:42 17.53 0.12 16.43 0.09 15.72 0.09 — ± ± ± a644 024 08:17:48.1 07:37:31 17.13 0.12 16.16 0.09 15.54 0.09 — ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
a644 025 08:17:42.6 07:39:35 19.70 0.12 18.81 0.09 18.14 0.09 — ± ± ± a2104 001 15:40:07.6 03:17:06 20.59 0.16 19.34 0.12 18.62 0.12 18.05 0.11 ± ± ± ± a2104 002 15:40:08.5 03:18:06 18.93 0.16 17.70 0.12 16.95 0.12 16.42 0.11 ± ± ± ± a2104 003 15:40:07.9 03:18:16 17.80 0.16 16.40 0.12 15.70 0.12 15.11 0.11 ± ± ± ± a2104 004 15:40:06.3 03:18:20 19.93 0.16 18.57 0.12 17.87 0.12 17.37 0.11 ± ± ± ± a2104 005 15:40:08.2 03:18:20 19.32 0.16 18.02 0.12 17.29 0.12 16.72 0.11 ± ± ± ± a2104 006 15:40:06.2 03:18:27 20.78 0.16 19.50 0.12 18.67 0.12 18.23 0.11 ± ± ± ± 29 a2104 007 15:40:08.5 03:16:56 19.51 0.16 18.23 0.12 17.50 0.12 16.91 0.11 ± ± ± ± a2104 008 15:40:05.2 03:18:29 19.21 0.16 17.88 0.12 17.10 0.12 16.55 0.11 ± ± ± ± a2104 009 15:40:11.2 03:17:56 20.25 0.16 18.94 0.12 18.17 0.12 17.58 0.11 ± ± ± ± a2104 010 15:40:02.1 03:17:22 19.18 0.16 17.91 0.12 17.20 0.12 16.64 0.11 ± ± ± ± a2104 011 15:40:03.2 03:18:35 20.65 0.16 19.47 0.12 18.68 0.12 18.16 0.11 ± ± ± ± a2104 012 15:40:02.0 03:17:06 18.58 0.16 17.32 0.12 16.63 0.12 16.07 0.11 ± ± ± ± a2104 013 15:40:03.9 03:18:46 18.37 0.16 16.99 0.12 16.27 0.12 15.67 0.11 ± ± ± ± a2104 014 15:40:10.5 03:16:39 19.00 0.16 17.82 0.12 17.11 0.12 16.56 0.11 ± ± ± ± a2104 015 15:40:07.3 03:19:00 19.50 0.16 18.54 0.12 17.90 0.12 17.40 0.11 ± ± ± ± a2104 016 15:40:11.6 03:16:54 20.52 0.16 19.36 0.12 18.65 0.12 18.15 0.11 ± ± ± ± a2104 017 15:40:05.9 03:19:08 18.68 0.16 17.37 0.12 16.65 0.12 16.06 0.11 ± ± ± ± a2104 018 15:40:10.0 03:18:57 19.74 0.16 18.46 0.12 17.71 0.12 17.12 0.11 ± ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
a2104 019 15:40:01.7 03:18:39 19.38 0.16 18.06 0.12 17.28 0.12 16.66 0.11 ± ± ± ± a2104 020 15:40:13.7 03:18:02 19.31 0.16 18.02 0.12 17.28 0.12 16.69 0.11 ± ± ± ± a2104 021 15:40:12.5 03:18:49 20.23 0.16 18.98 0.12 18.22 0.12 17.65 0.11 ± ± ± ± a2104 022 15:40:00.6 03:18:34 19.36 0.16 18.47 0.12 17.90 0.12 17.41 0.11 ± ± ± ± a2104 023 15:40:05.3 03:19:27 19.55 0.16 18.22 0.12 17.45 0.12 16.84 0.11 ± ± ± ± a2104 024 15:40:14.0 03:17:03 20.75 0.16 19.63 0.12 18.96 0.12 18.47 0.11 ± ± ± ± a2104 025 15:40:13.9 03:16:53 19.40 0.16 18.10 0.12 17.35 0.12 16.76 0.11 ± ± ± ± 30 a2104 026 15:39:58.8 03:17:19 19.86 0.16 18.62 0.12 17.93 0.12 17.36 0.11 ± ± ± ± a2104 027 15:40:05.1 03:19:39 19.22 0.16 17.93 0.12 17.20 0.12 16.63 0.11 ± ± ± ± a2104 028 15:40:04.3 03:19:37 20.11 0.16 18.83 0.12 18.08 0.12 17.50 0.11 ± ± ± ± a2104 029 15:40:15.0 03:16:48 21.27 0.16 20.08 0.12 19.32 0.12 18.76 0.11 ± ± ± ± a2104 030 15:40:09.1 03:19:51 19.80 0.16 18.54 0.12 17.82 0.12 17.24 0.11 ± ± ± ± a2104 031 15:40:10.1 03:19:52 20.25 0.16 19.07 0.12 18.33 0.12 17.79 0.11 ± ± ± ± a2104 032 15:40:09.4 03:15:18 18.20 0.16 16.84 0.12 16.13 0.12 15.54 0.11 ± ± ± ± a2104 033 15:40:16.7 03:18:10 20.85 0.16 19.68 0.12 18.93 0.12 18.40 0.11 ± ± ± ± a2104 034 15:39:59.7 03:19:35 19.20 0.16 17.88 0.12 17.15 0.12 16.56 0.11 ± ± ± ± a2104 035 15:40:03.1 03:20:11 18.76 0.16 17.61 0.12 16.95 0.12 16.39 0.11 ± ± ± ± a2104 036 15:39:56.1 03:18:29 21.13 0.16 19.89 0.12 19.10 0.12 18.51 0.11 ± ± ± ± a2104 037 15:39:56.0 03:18:36 21.17 0.16 19.87 0.12 19.14 0.12 18.57 0.11 ± ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
a2104 038 15:40:18.8 03:17:29 19.84 0.16 18.77 0.12 18.18 0.12 17.74 0.11 ± ± ± ± a2104 039 15:40:00.8 03:14:58 19.81 0.16 19.06 0.12 18.59 0.12 18.17 0.11 ± ± ± ± a2104 040 15:40:03.9 03:20:38 19.09 0.16 17.78 0.12 17.09 0.12 16.50 0.11 ± ± ± ± a2104 041 15:40:01.2 03:20:24 20.87 0.16 19.89 0.12 19.23 0.12 18.66 0.11 ± ± ± ± a2104 042 15:39:57.4 03:19:41 20.11 0.16 18.87 0.12 18.15 0.12 17.61 0.11 ± ± ± ± a2104 043 15:40:19.5 03:18:09 19.39 0.16 18.12 0.12 17.40 0.12 16.82 0.11 ± ± ± ± a2104 044 15:40:16.6 03:19:46 20.94 0.16 19.69 0.12 18.96 0.12 18.42 0.11 ± ± ± ± 31 a2104 045 15:40:18.6 03:16:15 19.37 0.16 18.31 0.12 17.63 0.12 17.04 0.11 ± ± ± ± a2104 046 15:40:19.5 03:18:24 20.53 0.16 19.51 0.12 18.87 0.12 18.41 0.11 ± ± ± ± a2104 047 15:40:07.2 03:14:22 19.47 0.16 18.31 0.12 17.57 0.12 16.90 0.11 ± ± ± ± a2104 048 15:40:00.4 03:20:32 20.88 0.16 19.57 0.12 18.82 0.12 18.25 0.11 ± ± ± ± a2104 049 15:39:54.8 03:19:08 19.78 0.16 18.48 0.12 17.73 0.12 17.12 0.11 ± ± ± ± a2104 050 15:40:20.8 03:17:49 20.04 0.16 18.79 0.12 18.09 0.12 17.51 0.11 ± ± ± ± a2104 051 15:40:16.7 03:15:07 19.90 0.16 18.73 0.12 17.98 0.12 17.41 0.11 ± ± ± ± a2104 052 15:40:20.3 03:18:53 19.83 0.16 18.77 0.12 18.13 0.12 17.59 0.11 ± ± ± ± a2104 053 15:39:52.9 03:18:44 20.38 0.16 19.62 0.12 19.10 0.12 18.64 0.11 ± ± ± ± a2104 054 15:40:11.1 03:21:11 19.60 0.16 18.44 0.12 17.76 0.12 17.23 0.11 ± ± ± ± a2104 055 15:40:19.2 03:19:42 19.98 0.16 18.67 0.12 17.91 0.12 17.32 0.11 ± ± ± ± a2104 056 15:40:00.1 03:14:19 20.75 0.16 19.59 0.12 18.83 0.12 18.29 0.11 ± ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
a2104 057 15:40:10.9 03:21:19 19.69 0.16 18.45 0.12 17.70 0.12 17.10 0.11 ± ± ± ± a2104 059 15:40:21.4 03:19:01 19.55 0.16 18.30 0.12 17.61 0.12 17.05 0.11 ± ± ± ± a2104 061 15:40:22.7 03:18:15 21.74 0.16 20.84 0.13 20.17 0.12 19.50 0.11 ± ± ± ± a2104 062 15:40:05.3 03:13:32 19.11 0.16 18.05 0.12 17.44 0.12 16.94 0.11 ± ± ± ± a2104 063 15:40:23.5 03:18:01 21.79 0.16 20.41 0.12 19.39 0.12 18.78 0.11 ± ± ± ± a2104 064 15:40:17.2 03:21:00 18.79 0.16 17.45 0.12 16.71 0.12 16.08 0.11 ± ± ± ± a2104 065 15:40:21.4 03:15:25 21.36 0.16 19.97 0.12 19.25 0.12 18.67 0.11 ± ± ± ± 32 a2104 067 15:40:19.4 03:20:42 19.18 0.16 17.88 0.12 17.14 0.12 16.52 0.11 ± ± ± ± a2104 069 15:40:24.1 03:20:08 19.63 0.16 18.41 0.12 17.69 0.12 17.12 0.11 ± ± ± ± a2104 070 15:39:50.5 03:20:48 20.53 0.16 19.31 0.12 18.61 0.12 18.05 0.11 ± ± ± ± a2104 072 15:40:25.4 03:20:33 19.93 0.16 18.87 0.12 18.18 0.12 17.57 0.11 ± ± ± ± a2104 073 15:40:26.3 03:14:56 19.70 0.16 19.36 0.12 18.93 0.12 18.78 0.11 ± ± ± ± a2104 074 15:40:20.7 03:13:08 18.23 0.16 17.14 0.12 16.51 0.12 15.96 0.11 ± ± ± ± a2104 075 15:40:23.6 03:13:47 18.57 0.16 17.33 0.12 16.59 0.12 15.93 0.11 ± ± ± ± a2104 076 15:40:05.3 03:23:23 18.70 0.16 17.35 0.12 16.68 0.12 16.10 0.11 ± ± ± ± a2104 077 15:40:12.6 03:11:42 18.77 0.16 17.68 0.12 17.06 0.12 16.53 0.11 ± ± ± ± a2104 079 15:40:01.6 03:24:09 18.44 0.16 17.40 0.12 16.79 0.12 16.23 0.11 ± ± ± ± a1689 004 13:11:29.5 01:20:27 17.87 0.14 16.48 0.10 15.75 0.10 15.16 0.09 ± ± ± ± a1689 008 13:11:28.6 01:20:26 19.10 0.14 17.74 0.10 17.02 0.10 16.45 0.10 ± ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
a1689 012 13:11:28.4 01:19:58 19.23 0.14 18.11 0.10 17.48 0.10 16.95 0.10 ± ± ± ± a1689 014 13:11:27.8 01:20:07 19.63 0.14 18.21 0.10 17.50 0.10 16.91 0.10 ± ± ± ± a1689 015 13:11:30.1 01:20:41 18.32 0.14 16.92 0.10 16.18 0.10 15.60 0.09 ± ± ± ± a1689 021 13:11:28.2 01:20:42 18.84 0.14 17.49 0.10 16.78 0.10 16.22 0.10 ± ± ± ± a1689 022 13:11:30.2 01:20:51 19.77 0.14 18.38 0.10 17.66 0.10 17.08 0.10 ± ± ± ± a1689 023 13:11:31.1 01:20:52 19.44 0.14 18.04 0.10 17.29 0.10 16.70 0.10 ± ± ± ± a1689 026 13:11:32.1 01:19:46 19.75 0.14 18.31 0.10 17.56 0.10 16.95 0.10 ± ± ± ± 33 a1689 027 13:11:32.7 01:19:58 18.51 0.14 17.05 0.10 16.28 0.10 15.68 0.09 ± ± ± ± a1689 030 13:11:31.4 01:19:31 18.80 0.14 17.34 0.10 16.61 0.10 15.97 0.09 ± ± ± ± a1689 031 13:11:26.2 01:19:56 20.57 0.14 19.14 0.10 18.40 0.10 17.82 0.10 ± ± ± ± a1689 036 13:11:26.0 01:19:51 20.62 0.14 19.26 0.10 18.54 0.10 17.97 0.10 ± ± ± ± a1689 038 13:11:29.1 01:21:16 19.31 0.14 17.93 0.10 17.19 0.10 16.61 0.10 ± ± ± ± a1689 039 13:11:29.3 01:19:16 20.70 0.14 19.41 0.10 18.73 0.10 18.17 0.10 ± ± ± ± a1689 041 13:11:25.4 01:20:17 20.90 0.14 19.72 0.10 19.09 0.10 18.50 0.10 ± ± ± ± a1689 045 13:11:25.4 01:20:36 19.48 0.14 18.06 0.10 17.32 0.10 16.72 0.09 ± ± ± ± a1689 049 13:11:28.8 01:19:02 21.41 0.14 20.09 0.10 19.41 0.10 18.82 0.10 ± ± ± ± a1689 050 13:11:25.2 01:19:31 20.98 0.14 19.66 0.10 18.98 0.10 18.40 0.10 ± ± ± ± a1689 052 13:11:34.1 01:21:01 19.44 0.14 18.40 0.10 17.88 0.10 17.45 0.10 ± ± ± ± a1689 055 13:11:34.8 01:20:59 20.52 0.14 19.15 0.10 18.42 0.10 17.82 0.10 ± ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
a1689 058 13:11:32.1 01:21:36 19.91 0.14 18.61 0.10 17.95 0.10 17.35 0.10 ± ± ± ± a1689 059 13:11:35.6 01:20:12 19.60 0.14 18.66 0.10 18.22 0.10 17.86 0.10 ± ± ± ± a1689 060 13:11:28.4 01:18:44 20.76 0.14 19.49 0.10 18.86 0.10 18.33 0.10 ± ± ± ± a1689 061 13:11:27.1 01:21:42 22.21 0.14 21.08 0.10 20.50 0.10 20.11 0.10 ± ± ± ± a1689 062 13:11:24.2 01:21:07 21.11 0.14 19.71 0.10 18.99 0.10 18.39 0.10 ± ± ± ± a1689 064 13:11:28.1 01:18:43 20.77 0.14 19.46 0.10 18.75 0.10 18.23 0.10 ± ± ± ± a1689 065 13:11:28.1 01:18:43 20.77 0.14 19.46 0.10 18.75 0.10 18.23 0.10 ± ± ± ± 34 a1689 067 13:11:27.1 01:18:48 21.86 0.14 20.72 0.10 20.01 0.10 19.52 0.10 ± ± ± ± a1689 069 13:11:29.1 01:21:55 20.52 0.14 19.12 0.10 18.40 0.10 17.81 0.10 ± ± ± ± a1689 070 13:11:29.4 01:18:34 20.44 0.14 19.09 0.10 18.39 0.10 17.83 0.10 ± ± ± ± a1689 071 13:11:32.7 01:18:41 19.09 0.14 18.05 0.10 17.53 0.10 17.11 0.09 ± ± ± ± a1689 072 13:11:24.1 01:19:06 21.22 0.14 19.88 0.10 19.20 0.10 18.62 0.10 ± ± ± ± a1689 074 13:11:36.6 01:19:42 20.72 0.14 19.68 0.10 19.05 0.10 18.52 0.10 ± ± ± ± a1689 076 13:11:30.0 01:22:07 19.85 0.14 18.51 0.10 17.79 0.10 17.23 0.10 ± ± ± ± a1689 077 13:11:33.8 01:18:44 20.23 0.14 18.89 0.10 18.20 0.10 17.63 0.10 ± ± ± ± a1689 078 13:11:26.6 01:22:00 20.56 0.14 19.32 0.10 18.67 0.10 18.15 0.10 ± ± ± ± a1689 079 13:11:35.4 01:21:32 20.02 0.14 18.59 0.10 17.86 0.10 17.26 0.09 ± ± ± ± a1689 080 13:11:27.1 01:22:08 20.45 0.14 19.04 0.10 18.31 0.10 17.70 0.10 ± ± ± ± a1689 083 13:11:32.2 01:22:10 20.19 0.14 18.80 0.10 18.11 0.10 17.52 0.10 ± ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
a1689 085 13:11:28.2 01:18:12 21.62 0.14 20.41 0.10 19.69 0.10 19.16 0.10 ± ± ± ± a1689 086 13:11:24.4 01:18:37 21.59 0.14 20.29 0.10 19.60 0.10 19.04 0.10 ± ± ± ± a1689 087 13:11:38.0 01:20:09 21.50 0.14 20.68 0.10 20.28 0.10 19.84 0.10 ± ± ± ± a1689 088 13:11:32.5 01:22:17 21.13 0.14 20.32 0.10 19.88 0.10 19.53 0.10 ± ± ± ± a1689 092 13:11:23.6 01:18:39 22.12 0.14 20.82 0.10 20.23 0.10 19.66 0.10 ± ± ± ± a1689 093 13:11:30.2 01:22:30 20.41 0.14 19.12 0.10 18.43 0.10 17.88 0.10 ± ± ± ± a1689 094 13:11:31.5 01:18:03 21.03 0.14 20.19 0.10 19.73 0.10 19.36 0.10 ± ± ± ± 35 a1689 095 13:11:37.9 01:19:20 19.55 0.14 18.23 0.10 17.53 0.10 16.98 0.09 ± ± ± ± a1689 096 13:11:38.3 01:21:04 19.86 0.14 18.75 0.10 18.18 0.10 17.68 0.10 ± ± ± ± a1689 097 13:11:36.6 01:18:46 21.64 0.14 20.42 0.10 19.82 0.10 19.34 0.10 ± ± ± ± a1689 099 13:11:37.6 01:21:39 21.90 0.14 20.70 0.10 19.98 0.10 19.45 0.10 ± ± ± ± a1689 100 13:11:23.1 01:22:04 23.02 0.15 22.00 0.12 21.31 0.10 20.82 0.11 ± ± ± ± a1689 103 13:11:34.5 01:18:11 19.31 0.14 18.21 0.10 17.65 0.10 17.15 0.10 ± ± ± ± a1689 105 13:11:27.4 01:22:47 20.48 0.14 19.12 0.10 18.39 0.10 17.76 0.10 ± ± ± ± a1689 106 13:11:29.8 01:17:42 22.20 0.14 21.02 0.10 20.37 0.10 19.77 0.10 ± ± ± ± a1689 107 13:11:22.7 01:22:12 22.07 0.14 21.45 0.10 20.86 0.10 20.55 0.10 ± ± ± ± a1689 109 13:11:34.1 01:22:34 23.93 0.18 22.83 0.14 22.07 0.11 21.41 0.12 ± ± ± ± a1689 110 13:11:25.7 01:17:52 21.27 0.14 20.36 0.10 19.98 0.10 19.70 0.10 ± ± ± ± a1689 111 13:11:38.2 01:21:41 23.56 0.19 23.30 0.26 22.42 0.14 21.61 0.17 ± ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
a1689 112 13:11:40.1 01:19:51 19.65 0.14 19.04 0.10 18.65 0.10 18.20 0.10 ± ± ± ± a1689 113 13:11:38.1 01:18:34 21.37 0.14 20.20 0.10 19.64 0.10 19.10 0.10 ± ± ± ± a1689 114 13:11:39.5 01:19:06 20.06 0.14 18.92 0.10 18.36 0.10 17.92 0.10 ± ± ± ± a1689 115 13:11:40.4 01:19:45 21.47 0.14 20.48 0.10 20.11 0.10 19.83 0.10 ± ± ± ± a1689 117 13:11:31.6 01:17:27 20.55 0.14 19.16 0.10 18.44 0.10 17.85 0.10 ± ± ± ± a1689 118 13:11:34.7 01:17:43 19.81 0.14 18.44 0.10 17.70 0.10 17.11 0.09 ± ± ± ± a1689 119 13:11:28.0 01:23:07 20.10 0.14 18.69 0.10 17.95 0.10 17.37 0.10 ± ± ± ± 36 a1689 120 13:11:29.8 01:17:21 20.49 0.14 19.10 0.10 18.38 0.10 17.80 0.10 ± ± ± ± a1689 121 13:11:37.0 01:22:31 21.41 0.14 20.23 0.10 19.55 0.10 18.98 0.10 ± ± ± ± a1689 122 13:11:35.5 01:17:42 20.55 0.14 19.20 0.10 18.49 0.10 17.93 0.10 ± ± ± ± a1689 123 13:11:17.7 01:20:34 21.50 0.14 20.60 0.10 20.27 0.10 20.05 0.10 ± ± ± ± a1689 124 13:11:38.0 01:18:08 19.42 0.14 18.01 0.10 17.28 0.10 16.69 0.09 ± ± ± ± a1689 126 13:11:20.5 01:22:21 22.38 0.14 21.72 0.11 21.28 0.10 20.98 0.11 ± ± ± ± a1689 127 13:11:33.0 01:23:13 22.17 0.14 20.93 0.10 20.38 0.10 19.78 0.10 ± ± ± ± a1689 128 13:11:19.4 01:18:30 21.18 0.14 19.89 0.10 19.22 0.10 18.68 0.10 ± ± ± ± a1689 129 13:11:37.9 01:22:36 20.90 0.14 19.58 0.10 18.92 0.10 18.35 0.10 ± ± ± ± a1689 130 13:11:36.6 01:22:53 19.89 0.14 18.97 0.10 18.41 0.10 17.90 0.10 ± ± ± ± a1689 131 13:11:35.7 01:17:30 21.73 0.14 21.32 0.10 21.03 0.10 21.17 0.12 ± ± ± ± a1689 132 13:11:42.1 01:19:34 22.27 0.14 20.84 0.10 19.71 0.10 18.96 0.10 ± ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
a1689 135 13:11:38.6 01:22:44 22.93 0.17 22.71 0.18 22.48 0.14 22.42 0.23 ± ± ± ± a1689 136 13:11:33.2 01:17:01 19.55 0.14 18.19 0.10 17.48 0.10 16.90 0.09 ± ± ± ± a1689 138 13:11:39.6 01:17:49 20.56 0.14 19.18 0.10 18.44 0.10 17.84 0.10 ± ± ± ± a1689 139 13:11:40.3 01:18:00 20.12 0.14 18.73 0.10 18.03 0.10 17.44 0.10 ± ± ± ± a1689 140 13:11:16.0 01:19:09 20.71 0.14 19.38 0.10 18.72 0.10 18.13 0.10 ± ± ± ± a1689 141 13:11:16.0 01:19:04 20.45 0.14 19.01 0.10 18.30 0.10 17.68 0.10 ± ± ± ± a1689 142 13:11:23.3 01:23:32 20.13 0.14 19.32 0.10 18.82 0.10 18.28 0.10 ± ± ± ± 37 a1689 143 13:11:43.4 01:19:19 19.34 0.14 17.91 0.10 17.19 0.10 16.59 0.09 ± ± ± ± a1689 144 13:11:32.6 01:23:50 19.93 0.14 18.55 0.10 17.83 0.10 17.23 0.09 ± ± ± ± a1689 145 13:11:21.0 01:23:16 22.25 0.14 21.59 0.11 21.00 0.10 20.63 0.11 ± ± ± ± a1689 147 13:11:37.2 01:17:07 20.51 0.14 19.11 0.10 18.41 0.10 17.81 0.10 ± ± ± ± a1689 148 13:11:20.0 01:23:08 23.34 0.16 22.31 0.12 21.53 0.10 20.87 0.11 ± ± ± ± a1689 149 13:11:36.0 01:23:40 19.88 0.14 18.46 0.10 17.73 0.10 17.10 0.09 ± ± ± ± a1689 150 13:11:18.0 01:22:47 20.41 0.14 19.06 0.10 18.36 0.10 17.77 0.10 ± ± ± ± a1689 151 13:11:30.1 01:16:25 20.75 0.14 19.50 0.10 18.79 0.10 18.18 0.10 ± ± ± ± a1689 153 13:11:35.8 01:23:56 21.32 0.14 20.70 0.10 19.80 0.10 19.23 0.10 ± ± ± ± a1689 155 13:11:22.8 01:23:54 21.42 0.14 20.71 0.10 20.40 0.10 20.16 0.10 ± ± ± ± a1689 156 13:11:43.8 01:18:22 23.14 0.15 22.42 0.12 21.93 0.11 21.62 0.12 ± ± ± ± a1689 158 13:11:13.6 01:19:34 19.35 0.14 18.03 0.10 17.33 0.10 16.77 0.09 ± ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
a1689 159 13:11:16.5 01:17:49 23.27 0.17 22.31 0.14 21.81 0.11 21.19 0.13 ± ± ± ± a1689 160 13:11:14.1 01:18:56 19.50 0.14 18.19 0.10 17.52 0.10 16.96 0.09 ± ± ± ± a1689 161 13:11:14.5 01:21:55 20.31 0.14 19.00 0.10 18.34 0.10 17.79 0.10 ± ± ± ± a1689 162 13:11:17.2 01:17:31 22.30 0.14 20.83 0.10 19.82 0.10 18.67 0.10 ± ± ± ± a1689 163 13:11:20.6 01:16:46 22.20 0.14 20.94 0.10 20.20 0.10 19.73 0.10 ± ± ± ± a1689 164 13:11:27.1 01:16:10 19.79 0.14 18.76 0.10 18.19 0.10 17.67 0.10 ± ± ± ± a1689 165 13:11:37.9 01:23:52 21.30 0.14 19.97 0.10 19.33 0.10 18.76 0.10 ± ± ± ± 38 a1689 166 13:11:15.9 01:17:44 21.11 0.14 19.90 0.10 19.27 0.10 18.77 0.10 ± ± ± ± a1689 167 13:11:39.4 01:16:49 19.75 0.14 18.39 0.10 17.68 0.10 17.09 0.09 ± ± ± ± a1689 168 13:11:27.4 01:24:30 19.23 0.14 17.84 0.10 17.13 0.10 16.54 0.09 ± ± ± ± a1689 170 13:11:35.5 01:24:28 20.97 0.14 19.64 0.10 18.93 0.10 18.35 0.10 ± ± ± ± a1689 171 13:11:32.8 01:24:40 22.17 0.14 21.05 0.10 20.49 0.10 20.02 0.10 ± ± ± ± a1689 172 13:11:28.1 01:15:49 21.76 0.14 20.64 0.10 19.98 0.10 19.41 0.10 ± ± ± ± a1689 173 13:11:36.7 01:24:22 22.99 0.15 21.77 0.11 21.03 0.10 20.49 0.10 ± ± ± ± a1689 174 13:11:25.0 01:24:39 21.16 0.14 20.66 0.10 20.31 0.10 20.08 0.10 ± ± ± ± a1689 175 13:11:44.1 01:17:33 22.76 0.15 22.06 0.12 21.69 0.10 21.26 0.12 ± ± ± ± a1689 177 13:11:28.4 01:24:56 23.18 0.15 22.00 0.11 21.07 0.10 20.29 0.10 ± ± ± ± a1689 178 13:11:22.5 01:24:43 22.02 0.14 21.40 0.10 21.17 0.10 21.04 0.11 ± ± ± ± a1689 179 13:11:34.8 01:25:02 23.19 0.15 22.22 0.11 21.31 0.10 20.60 0.10 ± ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
a1689 181 13:11:24.8 01:25:09 — — 19.92 0.10 — ± a1689 185 13:11:26.1 01:15:14 22.10 0.14 20.88 0.10 20.23 0.10 19.71 0.10 ± ± ± ± a1689 186 13:11:45.4 01:23:36 18.92 0.14 17.57 0.10 16.84 0.10 16.23 0.09 ± ± ± ± a1689 187 13:11:21.4 01:25:02 20.60 0.14 19.89 0.10 19.50 0.10 19.07 0.10 ± ± ± ± a1689 189 13:11:47.6 01:23:01 21.03 0.14 19.66 0.10 18.67 0.10 17.45 0.10 ± ± ± ± a1689 191 13:11:49.8 01:22:01 21.62 0.14 20.74 0.10 20.36 0.10 19.99 0.10 ± ± ± ± a1689 192 13:11:49.3 01:22:30 22.80 0.14 21.47 0.10 20.41 0.10 19.72 0.10 ± ± ± ± 39 a1689 194 13:11:51.2 01:20:38 22.00 0.14 20.78 0.10 19.85 0.10 19.08 0.10 ± ± ± ± a1689 195 13:11:50.1 01:22:24 20.26 0.14 19.46 0.10 19.02 0.10 18.54 0.10 ± ± ± ± a1689 196 13:11:36.4 01:25:38 21.35 0.14 20.00 0.10 19.30 0.10 18.70 0.10 ± ± ± ± a1689 198 13:11:49.6 01:17:31 22.99 0.15 21.96 0.11 21.35 0.10 20.86 0.10 ± ± ± ± a1689 200 13:11:51.9 01:21:38 20.00 0.14 18.70 0.10 17.78 0.10 16.71 0.09 ± ± ± ± a1689 201 13:11:51.6 01:21:56 22.00 0.14 20.92 0.10 20.37 0.10 19.89 0.10 ± ± ± ± a1689 204 13:11:37.4 01:25:46 20.67 0.14 19.80 0.10 19.35 0.10 18.93 0.10 ± ± ± ± a1689 207 13:11:35.2 01:26:08 22.05 0.14 21.20 0.10 20.71 0.10 20.21 0.10 ± ± ± ± a1689 209 13:11:48.0 01:24:19 19.64 0.14 18.25 0.10 17.55 0.10 16.96 0.09 ± ± ± ± a1689 211 13:11:53.7 01:21:33 21.97 0.14 21.24 0.10 20.77 0.10 20.44 0.10 ± ± ± ± a1689 215 13:11:52.3 01:23:02 22.48 0.14 21.07 0.10 20.12 0.10 19.20 0.10 ± ± ± ± a1689 217 13:11:43.8 01:15:00 19.95 0.14 18.86 0.10 18.16 0.10 17.45 0.10 ± ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
a1689 218 13:11:49.2 01:24:22 18.69 0.14 18.03 0.10 17.59 0.10 17.25 0.10 ± ± ± ± a1689 219 13:11:45.3 01:25:17 21.30 0.14 19.97 0.10 19.30 0.10 18.71 0.10 ± ± ± ± a1689 220 13:11:53.5 01:22:27 22.80 0.15 21.80 0.11 21.27 0.10 20.74 0.10 ± ± ± ± a1689 221 13:11:47.6 01:15:24 21.44 0.14 20.10 0.10 19.44 0.10 18.91 0.10 ± ± ± ± a1689 229 13:11:56.0 01:22:49 20.99 0.14 19.82 0.10 19.27 0.10 18.80 0.10 ± ± ± ± a1689 231 13:11:51.9 01:24:49 — — 22.55 0.14 21.44 0.14 ± ± a1689 233 13:11:55.5 01:15:41 20.13 0.14 18.83 0.10 18.14 0.10 17.59 0.10 ± ± ± ± 40 a1689 234 13:11:54.8 01:25:11 21.12 0.14 19.94 0.10 19.31 0.10 18.77 0.10 ± ± ± ± a1689 238 13:11:49.8 01:13:57 19.37 0.14 18.12 0.10 17.44 0.10 16.86 0.09 ± ± ± ± a1689 244 13:11:14.5 01:28:37 20.69 0.14 19.79 0.10 19.25 0.10 18.76 0.10 ± ± ± ± a1689 251 13:11:02.9 01:31:47 19.35 0.14 17.70 0.10 17.13 0.10 16.37 0.10 ± ± ± ± a1689 252 13:11:08.8 01:32:38 19.02 0.14 18.28 0.10 18.02 0.10 17.28 0.10 ± ± ± ± a2163 001 16:15:25.8 06:09:26 20.70 0.19 19.69 0.16 18.88 0.14 — ± ± ± a2163 002 16:15:24.4 06:09:03 17.96 0.19 17.29 0.15 16.78 0.14 — ± ± ± a2163 003 16:15:28.4 06:10:22 20.87 0.20 20.54 0.16 20.19 0.14 — ± ± ± a2163 004 16:15:21.7 06:08:34 21.41 0.20 20.31 0.16 19.64 0.14 — ± ± ± a2163 005 16:15:20.0 06:08:32 19.30 0.19 18.14 0.15 17.45 0.14 — ± ± ± a2163 006 16:15:35.3 06:11:15 19.79 0.19 18.59 0.15 17.86 0.14 — ± ± ± a2163 008 16:15:37.6 06:11:12 20.44 0.19 19.70 0.16 19.30 0.14 — ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
a2163 013 16:15:41.4 06:11:40 20.65 0.19 20.10 0.16 19.69 0.14 — ± ± ± a2163 015 16:15:30.6 06:12:29 19.98 0.19 18.74 0.15 18.03 0.14 — ± ± ± a2163 016 16:15:39.4 06:12:26 20.18 0.19 19.07 0.15 18.40 0.14 — ± ± ± a2163 019 16:15:23.9 06:12:15 22.04 0.21 21.14 0.17 20.69 0.15 — ± ± ± a2163 030 16:15:28.4 06:13:07 20.06 0.19 18.80 0.15 18.09 0.14 — ± ± ± a2163 039 16:15:48.4 06:12:37 21.37 0.20 20.50 0.16 19.68 0.14 — ± ± ± a2163 051 16:15:41.0 06:13:38 19.92 0.19 18.74 0.15 18.01 0.14 — ± ± ± 41 a2163 060 16:15:14.8 06:12:15 18.80 0.19 18.03 0.15 17.52 0.14 — ± ± ± a2163 075 16:15:53.7 06:13:07 20.62 0.19 19.45 0.15 18.72 0.14 — ± ± ± a2163 088 16:15:57.9 06:13:18 19.13 0.19 18.64 0.15 18.26 0.14 — ± ± ± a2163 091 16:15:48.9 06:15:12 20.68 0.19 19.60 0.16 19.00 0.14 — ± ± ± a2163 093 16:16:03.0 06:13:12 20.62 0.19 19.67 0.16 19.30 0.14 — ± ± ± a2163 094 16:15:34.0 06:16:50 19.96 0.19 18.74 0.15 18.02 0.14 — ± ± ± a2163 096 16:15:43.6 06:17:30 17.84 0.19 16.55 0.15 15.72 0.14 — ± ± ± a2163 097 16:15:37.3 06:17:44 21.18 0.20 20.07 0.16 19.45 0.14 — ± ± ± a2163 098 16:16:02.0 06:15:47 21.28 0.20 20.18 0.16 19.42 0.14 — ± ± ± a2163 101 16:15:59.9 06:16:42 21.32 0.20 20.77 0.16 20.17 0.14 — ± ± ± a2163 109 16:15:51.7 06:19:07 20.88 0.19 19.89 0.16 19.18 0.14 — ± ± ± a2163 110 16:15:55.3 06:19:22 20.64 0.19 19.52 0.15 18.74 0.14 — ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
a2163 111 16:15:39.2 06:20:27 19.61 0.19 18.36 0.15 17.62 0.14 — ± ± ± ms1008 001 10:10:34.1 12:39:52 22.58 0.12 21.11 0.09 20.10 0.08 19.41 0.08 ± ± ± ± ms1008 002 10:10:35.0 12:39:41 21.84 0.12 20.62 0.08 20.12 0.08 19.72 0.08 ± ± ± ± ms1008 003 10:10:33.5 12:39:59 21.68 0.12 20.71 0.09 19.71 0.08 19.05 0.08 ± ± ± ± ms1008 004 10:10:32.3 12:39:53 18.93 0.11 17.49 0.08 16.52 0.08 15.74 0.08 ± ± ± ± ms1008 005 10:10:32.3 12:39:34 20.62 0.12 19.18 0.08 18.25 0.08 17.58 0.08 ± ± ± ± ms1008 006 10:10:32.1 12:40:01 20.68 0.12 19.25 0.08 18.27 0.08 17.63 0.08 ± ± ± ± 42 ms1008 007 10:10:31.8 12:39:59 21.76 0.12 20.48 0.09 19.57 0.08 18.95 0.08 ± ± ± ± ms1008 008 10:10:35.3 12:40:21 21.48 0.12 19.94 0.08 18.93 0.08 18.18 0.08 ± ± ± ± ms1008 009 10:10:36.6 12:40:04 21.09 0.12 20.23 0.08 19.69 0.08 19.21 0.08 ± ± ± ± ms1008 010 10:10:32.6 12:40:22 21.47 0.12 19.99 0.08 18.98 0.08 18.32 0.08 ± ± ± ± ms1008 011 10:10:32.9 12:39:09 21.52 0.12 20.06 0.08 19.10 0.08 18.41 0.08 ± ± ± ± ms1008 012 10:10:32.8 12:40:34 21.96 0.12 20.72 0.08 19.81 0.08 19.16 0.08 ± ± ± ± ms1008 013 10:10:36.4 12:40:28 22.92 0.13 21.59 0.09 20.79 0.08 20.25 0.08 ± ± ± ± ms1008 014 10:10:37.2 12:40:20 21.36 0.12 19.98 0.08 19.10 0.08 18.48 0.08 ± ± ± ± ms1008 015 10:10:35.1 12:38:53 20.78 0.12 19.66 0.08 19.17 0.08 18.73 0.08 ± ± ± ± ms1008 016 10:10:30.5 12:40:19 21.73 0.12 20.50 0.08 19.88 0.08 19.40 0.08 ± ± ± ± ms1008 017 10:10:37.7 12:39:10 22.77 0.12 21.37 0.09 20.50 0.08 19.90 0.08 ± ± ± ± ms1008 018 10:10:29.5 12:39:50 22.07 0.12 20.74 0.08 19.88 0.08 19.30 0.08 ± ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
ms1008 019 10:10:29.5 12:40:04 21.84 0.12 20.40 0.08 19.42 0.08 18.77 0.08 ± ± ± ± ms1008 020 10:10:29.8 12:40:16 22.63 0.12 21.22 0.09 20.31 0.08 19.66 0.08 ± ± ± ± ms1008 021 10:10:38.7 12:40:17 22.61 0.12 21.27 0.09 20.48 0.08 19.89 0.08 ± ± ± ± ms1008 022 10:10:29.2 12:39:36 21.35 0.12 19.90 0.08 18.93 0.08 18.23 0.08 ± ± ± ± ms1008 023 10:10:39.5 12:39:40 21.91 0.12 20.65 0.08 19.70 0.08 19.04 0.08 ± ± ± ± ms1008 024 10:10:28.1 12:40:10 22.56 0.12 21.20 0.09 20.41 0.08 19.63 0.08 ± ± ± ± ms1008 025 10:10:39.8 12:40:34 22.20 0.12 20.77 0.08 19.87 0.08 19.20 0.08 ± ± ± ± 43 ms1008 029 10:10:28.5 12:41:01 21.71 0.12 20.35 0.08 19.50 0.08 18.87 0.08 ± ± ± ± ms1008 030 10:10:40.2 12:41:00 21.08 0.12 19.91 0.08 19.27 0.08 18.68 0.08 ± ± ± ± ms1008 031 10:10:38.6 12:41:31 21.08 0.12 19.91 0.08 19.23 0.08 18.62 0.08 ± ± ± ± ms1008 033 10:10:31.7 12:37:47 20.78 0.12 19.32 0.08 18.37 0.08 17.65 0.08 ± ± ± ± ms1008 034 10:10:42.5 12:39:09 21.38 0.12 20.39 0.08 19.89 0.08 19.41 0.08 ± ± ± ± ms1008 035 10:10:42.0 12:38:54 20.42 0.12 19.06 0.08 18.13 0.08 — ± ± ± ms1008 036 10:10:31.3 12:37:44 23.11 0.12 21.56 0.09 20.57 0.08 19.95 0.08 ± ± ± ± ms1008 037 10:10:39.5 12:41:32 23.15 0.14 22.06 0.10 21.56 0.09 21.46 0.11 ± ± ± ± ms1008 039 10:10:34.1 12:42:02 21.24 0.12 19.89 0.08 18.97 0.08 18.28 0.08 ± ± ± ± ms1008 040 10:10:29.5 12:37:44 22.86 0.13 21.51 0.09 20.57 0.08 19.93 0.08 ± ± ± ± ms1008 041 10:10:33.5 12:37:26 22.14 0.12 20.79 0.09 20.00 0.08 19.45 0.08 ± ± ± ± ms1008 043 10:10:26.4 12:38:10 21.59 0.12 20.30 0.08 19.44 0.08 18.80 0.08 ± ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
ms1008 044 10:10:32.9 12:37:18 20.47 0.12 19.20 0.08 18.30 0.08 17.74 0.08 ± ± ± ± ms1008 045 10:10:28.6 12:37:37 22.16 0.12 20.80 0.08 19.97 0.08 19.39 0.08 ± ± ± ± ms1008 046 10:10:29.0 12:37:34 21.87 0.12 20.64 0.08 20.05 0.08 19.58 0.08 ± ± ± ± ms1008 048 10:10:30.5 12:37:23 20.52 0.12 19.14 0.08 18.39 0.08 17.74 0.08 ± ± ± ± ms1008 049 10:10:33.4 12:42:21 21.80 0.12 20.42 0.08 19.51 0.08 18.83 0.08 ± ± ± ± ms1008 050 10:10:44.8 12:39:12 22.37 0.12 20.98 0.09 20.11 0.08 19.47 0.08 ± ± ± ± ms1008 051 10:10:30.8 12:37:13 22.43 0.12 20.93 0.09 19.95 0.08 19.32 0.08 ± ± ± ± 44 ms1008 052 10:10:33.2 12:37:03 21.34 0.12 19.92 0.09 18.96 0.08 18.34 0.08 ± ± ± ± ms1008 053 10:10:45.4 12:39:40 21.45 0.12 20.11 0.08 19.17 0.08 18.69 0.08 ± ± ± ± ms1008 055 10:10:34.6 12:36:53 21.61 0.12 20.38 0.08 19.40 0.08 18.76 0.08 ± ± ± ± ms1008 056 10:10:31.1 12:37:01 19.79 0.11 18.35 0.08 17.39 0.08 16.72 0.08 ± ± ± ± ms1008 057 10:10:31.5 12:42:37 21.60 0.12 20.21 0.09 19.21 0.08 18.47 0.08 ± ± ± ± ms1008 058 10:10:30.4 12:36:59 21.28 0.12 20.00 0.08 19.25 0.08 18.70 0.08 ± ± ± ± ms1008 059 10:10:35.6 12:36:49 21.17 0.12 19.83 0.08 18.92 0.08 18.19 0.08 ± ± ± ± ms1008 060 10:10:27.8 12:37:06 22.27 0.12 20.85 0.08 19.93 0.08 19.23 0.08 ± ± ± ± ms1008 062 10:10:34.3 12:36:39 22.30 0.12 20.79 0.08 19.86 0.08 19.14 0.08 ± ± ± ± ms1008 063 10:10:22.4 12:40:52 21.41 0.12 20.64 0.08 20.27 0.08 19.98 0.08 ± ± ± ± ms1008 064 10:10:31.1 12:42:47 21.75 0.12 20.48 0.08 19.80 0.08 19.26 0.08 ± ± ± ± ms1008 066 10:10:27.8 12:42:30 21.04 0.12 19.62 0.08 18.68 0.08 17.94 0.08 ± ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
ms1008 068 10:10:34.2 12:36:32 23.56 0.14 22.14 0.10 21.46 0.09 20.92 0.09 ± ± ± ± ms1008 070 10:10:35.0 12:43:07 21.53 0.12 20.15 0.08 19.21 0.08 18.49 0.08 ± ± ± ± ms1008 075 10:10:27.6 12:36:37 22.51 0.12 21.07 0.08 20.09 0.08 19.39 0.08 ± ± ± ± ms1008 076 10:10:23.2 12:37:26 21.92 0.12 20.56 0.08 19.63 0.08 18.95 0.08 ± ± ± ± ms1008 078 10:10:19.2 12:39:35 22.21 0.12 20.83 0.08 19.89 0.08 19.18 0.08 ± ± ± ± ms1008 087 10:10:17.1 12:40:23 22.15 0.12 20.82 0.08 20.01 0.08 19.41 0.08 ± ± ± ± ms1008 089 10:10:18.7 12:37:43 21.39 0.12 20.08 0.08 19.38 0.08 18.75 0.08 ± ± ± ± 45 ms1008 094 10:10:17.8 12:36:06 22.13 0.12 20.79 0.08 19.88 0.08 19.16 0.08 ± ± ± ± ms1008 095 10:10:11.0 12:41:28 23.29 0.13 22.03 0.10 21.23 0.09 20.66 0.09 ± ± ± ± ms1008 096 10:10:05.2 12:38:34 — — 21.41 0.09 20.74 0.09 ± ± ac114 001 22:58:52.3 34:46:47 — 22.40 0.12 21.73 0.10 21.40 0.11 ± ± ± ac114 002 22:58:51.0 34:46:58 21.04 0.12 20.16 0.08 19.60 0.08 19.11 0.08 ± ± ± ± ac114 003 22:58:49.9 34:46:41 21.87 0.12 20.44 0.08 19.41 0.08 18.68 0.08 ± ± ± ± ac114 004 22:58:49.3 34:47:01 21.10 0.11 19.94 0.08 19.11 0.08 18.40 0.08 ± ± ± ± ac114 005 22:58:49.5 34:47:09 — 22.26 0.13 21.17 0.09 20.42 0.09 ± ± ± ac114 006 22:58:49.1 34:47:02 21.66 0.12 20.20 0.08 19.15 0.08 18.38 0.08 ± ± ± ± ac114 007 22:58:53.0 34:46:13 23.00 0.17 22.11 0.12 21.87 0.10 21.73 0.12 ± ± ± ± ac114 008 22:58:48.9 34:46:56 22.37 0.14 20.82 0.08 19.77 0.08 19.06 0.08 ± ± ± ± ac114 009 22:58:48.7 34:47:11 23.15 0.21 22.05 0.12 20.83 0.08 20.08 0.09 ± ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
ac114 010 22:58:55.9 34:47:16 22.62 0.14 21.23 0.09 20.26 0.08 19.59 0.08 ± ± ± ± ac114 011 22:58:50.3 34:47:38 — 22.24 0.12 21.29 0.09 20.61 0.09 ± ± ± ac114 012 22:58:49.0 34:47:23 — 22.04 0.11 20.95 0.09 20.17 0.09 ± ± ± ac114 014 22:58:50.1 34:47:45 — — 21.78 0.11 20.97 0.11 ± ± ac114 015 22:58:48.1 34:47:21 — — 21.65 0.12 21.23 0.12 ± ± ac114 016 22:58:48.0 34:47:25 21.43 0.12 19.86 0.08 18.75 0.08 17.96 0.08 ± ± ± ± ac114 017 22:58:55.1 34:45:55 22.44 0.14 20.98 0.08 20.01 0.08 19.27 0.08 ± ± ± ± 46 ac114 018 22:58:50.0 34:47:56 21.95 0.12 20.51 0.08 19.42 0.08 18.66 0.08 ± ± ± ± ac114 019 22:58:46.7 34:46:50 — — 22.65 0.20 22.12 0.24 ± ± ac114 020 22:58:50.9 34:48:01 21.55 0.12 19.97 0.08 18.83 0.08 18.04 0.08 ± ± ± ± ac114 021 22:58:46.3 34:46:43 21.09 0.11 20.00 0.08 19.18 0.08 18.48 0.08 ± ± ± ± ac114 022 22:58:47.5 34:47:42 — 22.31 0.14 21.18 0.09 20.51 0.09 ± ± ± ac114 023 22:58:46.6 34:47:30 21.53 0.12 20.64 0.08 19.98 0.08 19.41 0.08 ± ± ± ± ac114 025 22:58:46.5 34:46:17 21.86 0.12 20.67 0.08 20.09 0.08 19.66 0.08 ± ± ± ± ac114 026 22:58:46.3 34:47:29 21.73 0.12 20.25 0.08 19.22 0.08 18.48 0.08 ± ± ± ± ac114 028 22:58:50.0 34:48:13 21.68 0.12 20.56 0.08 19.93 0.08 19.49 0.08 ± ± ± ± ac114 029 22:58:57.0 34:47:57 22.33 0.13 21.04 0.08 20.43 0.08 19.80 0.08 ± ± ± ± ac114 030 22:58:46.9 34:47:49 22.09 0.13 20.72 0.08 19.60 0.08 18.86 0.08 ± ± ± ± ac114 031 22:58:52.3 34:48:21 — 22.45 0.13 21.42 0.09 20.80 0.09 ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
ac114 032 22:58:53.7 34:45:28 — — 21.85 0.10 20.94 0.10 ± ± ac114 033 22:58:48.4 34:48:08 19.64 0.11 18.08 0.08 16.90 0.08 16.09 0.08 ± ± ± ± ac114 035 22:58:46.6 34:47:47 22.51 0.14 21.04 0.08 19.93 0.08 19.18 0.08 ± ± ± ± ac114 036 22:58:55.7 34:45:35 21.77 0.12 20.36 0.08 19.38 0.08 18.64 0.08 ± ± ± ± ac114 037 22:58:58.9 34:46:15 22.55 0.15 21.77 0.10 21.26 0.09 20.76 0.09 ± ± ± ± ac114 038 22:58:45.3 34:47:26 22.47 0.13 21.02 0.08 19.95 0.08 19.22 0.08 ± ± ± ± ac114 039 22:58:45.3 34:46:20 — 21.97 0.10 20.87 0.08 20.25 0.08 ± ± ± 47 ac114 040 22:58:47.1 34:48:01 22.43 0.14 20.93 0.09 19.81 0.08 19.05 0.08 ± ± ± ± ac114 041 22:58:47.8 34:48:11 21.59 0.13 19.87 0.08 18.71 0.08 17.93 0.08 ± ± ± ± ac114 042 22:58:46.6 34:47:59 21.56 0.12 20.10 0.08 19.03 0.08 18.27 0.08 ± ± ± ± ac114 043 22:58:48.0 34:48:19 — 21.81 0.17 20.45 0.11 19.67 0.10 ± ± ± ac114 044 22:58:44.4 34:47:22 — — 22.79 0.15 22.45 0.18 ± ± ac114 045 22:58:44.0 34:46:28 22.11 0.13 20.72 0.08 19.59 0.08 18.85 0.08 ± ± ± ± ac114 046 22:58:57.2 34:48:21 23.61 0.23 21.92 0.10 20.81 0.08 20.06 0.08 ± ± ± ± ac114 048 22:58:46.0 34:48:09 22.46 0.14 21.08 0.09 20.02 0.08 19.27 0.08 ± ± ± ± ac114 049 22:58:52.9 34:48:45 21.75 0.12 20.55 0.08 19.97 0.08 19.51 0.08 ± ± ± ± ac114 050 22:58:43.0 34:46:38 22.32 0.14 21.02 0.09 19.99 0.08 19.27 0.08 ± ± ± ± ac114 051 22:58:52.1 34:48:50 23.08 0.17 21.65 0.09 20.59 0.08 19.89 0.08 ± ± ± ± ac114 052 22:58:46.5 34:45:23 21.75 0.12 20.29 0.08 19.28 0.08 18.53 0.08 ± ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
ac114 053 22:58:47.0 34:48:31 22.18 0.13 20.62 0.08 19.49 0.08 18.68 0.08 ± ± ± ± ac114 054 22:58:44.0 34:47:53 — — 21.69 0.10 20.97 0.10 ± ± ac114 055 22:58:43.9 34:47:50 23.12 0.17 22.07 0.11 21.13 0.09 20.45 0.09 ± ± ± ± ac114 056 22:58:45.3 34:48:15 — 21.85 0.10 20.74 0.08 19.99 0.08 ± ± ± ac114 058 22:58:44.3 34:48:15 — 21.65 0.09 20.57 0.08 19.84 0.08 ± ± ± ac114 059 22:58:44.5 34:48:18 — 22.03 0.11 21.04 0.08 20.28 0.08 ± ± ± ac114 060 22:58:42.9 34:45:57 — — 21.84 0.10 21.13 0.11 ± ± 48 ac114 061 22:58:43.2 34:45:49 — — 21.75 0.10 21.01 0.10 ± ± ac114 062 22:58:41.7 34:46:46 22.26 0.13 20.69 0.08 19.76 0.08 19.09 0.08 ± ± ± ± ac114 063 22:58:42.3 34:47:40 23.18 0.24 21.61 0.11 20.50 0.09 19.68 0.09 ± ± ± ± ac114 064 22:58:49.2 34:49:02 22.95 0.18 21.60 0.09 20.51 0.08 19.78 0.08 ± ± ± ± ac114 065 22:58:58.3 34:48:47 — 21.69 0.10 20.83 0.08 20.08 0.08 ± ± ± ac114 066 22:58:42.0 34:47:46 20.46 0.11 18.93 0.08 17.83 0.08 17.05 0.08 ± ± ± ± ac114 067 22:58:51.4 34:49:11 — 22.34 0.12 21.33 0.10 20.59 0.09 ± ± ± ac114 068 22:58:50.6 34:49:11 22.06 0.12 20.54 0.08 19.48 0.08 18.73 0.08 ± ± ± ± ac114 069 22:58:52.4 34:44:31 21.91 0.12 20.58 0.08 19.67 0.08 18.98 0.08 ± ± ± ± ac114 071 22:58:41.0 34:46:20 21.49 0.12 20.54 0.08 19.60 0.08 18.89 0.08 ± ± ± ± ac114 072 22:59:04.0 34:47:08 22.37 0.13 21.36 0.09 20.60 0.08 20.02 0.08 ± ± ± ± ac114 073 22:58:41.2 34:46:11 22.77 0.16 21.98 0.11 21.13 0.09 20.07 0.08 ± ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
ac114 074 22:58:41.6 34:48:06 21.98 0.12 20.46 0.08 19.36 0.08 18.59 0.08 ± ± ± ± ac114 075 22:58:56.1 34:49:17 22.16 0.13 20.72 0.08 19.61 0.08 18.85 0.08 ± ± ± ± ac114 076 22:58:42.8 34:48:30 22.39 0.13 21.05 0.09 20.53 0.08 20.12 0.08 ± ± ± ± ac114 077 22:58:42.7 34:45:20 22.15 0.12 20.68 0.08 19.74 0.08 19.00 0.08 ± ± ± ± ac114 078 22:58:46.6 34:49:09 — 21.65 0.10 20.65 0.08 19.94 0.08 ± ± ± ac114 081 22:58:40.6 34:47:52 20.94 0.12 19.43 0.08 18.34 0.08 17.55 0.08 ± ± ± ± ac114 082 22:58:48.3 34:49:23 22.87 0.16 21.87 0.10 20.86 0.08 20.14 0.08 ± ± ± ± 49 ac114 083 22:58:39.6 34:47:14 21.86 0.12 20.41 0.08 19.27 0.08 18.49 0.08 ± ± ± ± ac114 084 22:58:44.6 34:49:03 — — 21.62 0.10 21.02 0.10 ± ± ac114 086 22:58:43.1 34:48:47 21.60 0.12 20.57 0.08 20.10 0.08 19.65 0.08 ± ± ± ± ac114 089 22:58:54.3 34:49:35 22.11 0.12 20.60 0.08 19.47 0.08 18.69 0.08 ± ± ± ± ac114 090 22:58:56.7 34:49:28 22.56 0.15 21.33 0.09 20.07 0.08 19.25 0.08 ± ± ± ± ac114 091 22:58:39.8 34:47:49 23.10 0.17 21.25 0.09 20.26 0.08 19.56 0.08 ± ± ± ± ac114 092 22:58:40.3 34:45:46 — 22.42 0.14 21.66 0.10 21.03 0.10 ± ± ± ac114 093 22:58:48.8 34:44:15 21.61 0.12 20.21 0.08 19.17 0.08 18.42 0.08 ± ± ± ± ac114 094 22:58:50.3 34:49:38 22.59 0.14 20.95 0.08 19.91 0.08 19.17 0.08 ± ± ± ± ac114 095 22:58:44.6 34:49:11 21.07 0.11 19.60 0.08 18.56 0.08 17.83 0.08 ± ± ± ± ac114 100 22:58:42.0 34:44:48 20.79 0.11 20.00 0.08 19.60 0.08 19.24 0.08 ± ± ± ± ac114 102 22:58:41.9 34:49:05 22.39 0.13 21.46 0.09 21.07 0.09 20.60 0.09 ± ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
ac114 103 22:58:47.7 34:49:51 — — 21.76 0.11 21.06 0.11 ± ± ac114 105 22:58:46.6 34:44:02 22.98 0.17 21.35 0.09 20.31 0.08 19.58 0.08 ± ± ± ± ac114 106 22:58:46.2 34:49:44 23.28 0.20 21.86 0.11 20.89 0.08 20.24 0.09 ± ± ± ± ac114 107 22:58:55.1 34:49:58 21.43 0.12 19.87 0.08 18.75 0.08 17.98 0.08 ± ± ± ± ac114 110 22:58:38.0 34:48:11 23.00 0.16 21.88 0.11 20.86 0.08 20.21 0.08 ± ± ± ± ac114 111 22:59:00.1 34:49:42 23.10 0.19 — 21.89 0.11 21.48 0.12 ± ± ± ac114 112 22:58:43.4 34:49:36 21.71 0.12 20.59 0.08 20.03 0.08 19.56 0.08 ± ± ± ± 50 ac114 114 22:58:53.0 34:50:12 23.08 0.18 21.66 0.10 20.63 0.08 19.93 0.08 ± ± ± ± ac114 115 22:58:38.0 34:45:21 20.18 0.11 18.62 0.08 17.48 0.08 16.68 0.08 ± ± ± ± ac114 116 22:58:57.6 34:50:05 21.28 0.12 20.26 0.08 19.64 0.08 19.01 0.08 ± ± ± ± ac114 118 22:58:53.7 34:50:17 22.28 0.13 20.93 0.08 20.02 0.08 19.38 0.08 ± ± ± ± ac114 119 22:58:50.0 34:50:15 — — 21.90 0.11 21.17 0.11 ± ± ac114 120 22:58:37.3 34:48:20 21.96 0.12 — 19.98 0.08 19.44 0.08 ± ± ± ac114 121 22:58:59.4 34:50:01 22.85 0.16 21.19 0.09 20.14 0.08 19.36 0.08 ± ± ± ± ac114 122 22:58:37.7 34:48:38 — — 22.40 0.15 22.23 0.18 ± ± ac114 123 22:59:05.3 34:49:08 21.48 0.12 20.28 0.08 19.52 0.08 18.83 0.08 ± ± ± ± ac114 124 22:58:47.9 34:50:17 21.88 0.12 20.54 0.08 19.93 0.08 19.41 0.08 ± ± ± ± ac114 126 22:58:39.2 34:44:36 21.38 0.12 19.85 0.08 18.76 0.08 17.96 0.08 ± ± ± ± ac114 127 22:58:35.9 34:45:46 20.46 0.11 19.14 0.08 18.17 0.08 17.41 0.08 ± ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
ac114 129 22:58:34.8 34:47:04 22.63 0.14 21.19 0.09 20.23 0.08 19.54 0.08 ± ± ± ± ac114 130 22:58:47.3 34:50:23 23.16 0.18 21.75 0.10 21.03 0.09 20.46 0.09 ± ± ± ± ac114 131 22:58:42.2 34:43:55 — — 21.56 0.10 20.91 0.10 ± ± ac114 132 22:58:45.2 34:50:14 22.07 0.12 20.58 0.08 19.57 0.08 18.82 0.08 ± ± ± ± ac114 135 22:58:36.4 34:45:14 22.45 0.14 20.95 0.08 19.87 0.08 19.06 0.08 ± ± ± ± ac114 137 22:58:41.4 34:49:50 — — 22.24 0.13 20.51 0.11 ± ± ac114 138 22:58:58.9 34:50:20 20.77 0.11 20.06 0.08 19.68 0.08 19.34 0.08 ± ± ± ± 51 ac114 139 22:58:34.0 34:46:52 21.64 0.12 20.45 0.08 19.66 0.08 18.97 0.08 ± ± ± ± ac114 140 22:58:57.4 34:50:32 21.40 0.12 19.92 0.08 18.84 0.08 18.07 0.08 ± ± ± ± ac114 141 22:59:06.4 34:49:20 22.55 0.14 22.36 0.12 21.64 0.10 20.97 0.10 ± ± ± ± ac114 142 22:58:52.1 34:50:41 21.74 0.12 20.97 0.08 20.45 0.08 20.03 0.08 ± ± ± ± ac114 143 22:58:34.2 34:47:37 — 22.31 0.14 21.67 0.10 21.13 0.11 ± ± ± ac114 146 22:59:03.8 34:49:56 — 22.32 0.13 21.31 0.09 20.56 0.09 ± ± ± ac114 147 22:58:41.6 34:43:45 — — 21.35 0.09 21.19 0.11 ± ± ac114 149 22:58:33.5 34:46:24 20.52 0.11 19.43 0.08 18.72 0.08 18.02 0.08 ± ± ± ± ac114 150 22:58:37.8 34:49:24 21.84 0.12 20.47 0.08 19.50 0.08 18.76 0.08 ± ± ± ± ac114 152 22:59:04.6 34:49:55 22.77 0.15 21.20 0.09 20.20 0.08 19.51 0.08 ± ± ± ± ac114 153 22:58:39.6 34:49:55 22.22 0.13 21.58 0.10 21.06 0.09 20.70 0.09 ± ± ± ± ac114 154 22:58:39.1 34:49:56 23.20 0.18 22.10 0.11 21.10 0.09 20.40 0.09 ± ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
ac114 155 22:58:57.5 34:50:51 22.84 0.18 21.82 0.11 21.07 0.09 20.46 0.09 ± ± ± ± ac114 156 22:58:40.7 34:50:13 21.42 0.12 20.49 0.08 19.92 0.08 19.34 0.08 ± ± ± ± ac114 157 22:58:36.6 34:49:25 — — 23.11 0.23 22.53 0.29 ± ± ac114 160 22:58:46.2 34:50:56 — — 22.16 0.12 21.56 0.13 ± ± ac114 161 22:58:57.7 34:51:00 22.27 0.13 20.96 0.08 19.79 0.08 19.01 0.08 ± ± ± ± ac114 162 22:58:35.1 34:44:31 — 22.31 0.12 21.31 0.09 20.54 0.09 ± ± ± ac114 163 22:58:47.3 34:51:07 21.86 0.12 20.40 0.08 19.57 0.08 18.92 0.08 ± ± ± ± 52 ac114 164 22:58:45.0 34:51:00 22.25 0.13 21.20 0.09 20.55 0.08 20.02 0.08 ± ± ± ± ac114 165 22:58:50.5 34:51:15 — 22.21 0.12 21.23 0.09 20.62 0.09 ± ± ± ac114 166 22:58:37.5 34:50:15 — — 22.25 0.13 21.88 0.14 ± ± ac114 167 22:58:30.1 34:47:21 20.88 0.11 19.94 0.08 19.29 0.08 18.65 0.08 ± ± ± ± ac114 169 22:59:06.1 34:50:44 21.45 0.12 19.97 0.08 18.87 0.08 18.10 0.08 ± ± ± ± ac114 170 22:58:48.6 34:51:38 21.87 0.12 20.78 0.08 20.12 0.08 19.52 0.08 ± ± ± ± ac114 174 22:58:33.0 34:44:05 21.92 0.12 20.96 0.09 20.56 0.08 20.14 0.08 ± ± ± ± ac114 176 22:58:59.4 34:51:36 22.97 0.17 22.07 0.11 20.77 0.08 19.90 0.08 ± ± ± ± ac114 177 22:58:32.7 34:44:05 — — 21.20 0.09 20.41 0.09 ± ± ac114 178 22:58:44.6 34:51:33 20.04 0.11 19.14 0.08 18.52 0.08 17.93 0.08 ± ± ± ± ac114 181 22:58:47.6 34:51:46 23.23 0.19 21.79 0.10 20.76 0.08 20.02 0.08 ± ± ± ± ac114 182 22:58:28.4 34:46:00 22.04 0.13 — 19.61 0.08 18.89 0.08 ± ± ± (continued) Table 2.3—Continued
Name RA Dec B V R I (Vega) (Vega) (Vega) (Vega) (1) (2) (3) (4) (5) (6) (7)
ac114 185 22:58:30.2 34:44:40 — — 22.72 0.16 22.10 0.17 ± ± ac114 188 22:58:58.9 34:51:53 21.23 0.12 20.25 0.08 19.62 0.08 19.04 0.08 ± ± ± ± ac114 190 22:59:00.9 34:51:50 — 21.95 0.12 20.86 0.09 20.15 0.09 ± ± ± ac114 191 22:58:41.0 34:51:36 23.16 0.20 22.25 0.12 21.49 0.10 20.86 0.10 ± ± ± ± ac114 192 22:59:01.6 34:51:50 21.76 0.12 21.04 0.09 20.59 0.08 20.13 0.08 ± ± ± ±
53 ac114 193 22:59:01.2 34:51:53 22.34 0.14 21.61 0.10 21.17 0.09 20.73 0.10 ± ± ± ± ac114 197 22:58:59.8 34:52:17 20.12 0.11 19.35 0.08 18.83 0.08 18.32 0.08 ± ± ± ± ac114 199 22:58:56.7 34:52:28 21.89 0.12 20.94 0.09 20.38 0.08 19.90 0.08 ± ± ± ± ac114 202 22:58:57.5 34:52:30 22.65 0.16 22.06 0.13 21.72 0.11 21.30 0.12 ± ± ± ±
Note. — Visible photometry for identified cluster members. (1) The name of this object, constructed from a shorthand of its parent cluster and the order in which each object appears in the list of cluster members extracted from NED. (2 3) Positions of this object in J2000 coordinates, as derived from the R band images. (4 7) Visible photometry for each object, where detectable, in Vega magnitudes. Fluxes are measured in the R bandKron like aperture. Objects with no quoted magnitudes in a given band have either no coverage or no detection in that band. No upper limits are quoted. Name Fν(3.6 m) Fν(4.5 m) Fν(5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
a3128 001 1.37 0.08 0.90 0.08 0.66 0.17 < 0.77 < 0.84 ± ± ± a3128 002 2.84 0.17 1.82 0.13 1.28 0.20 < 0.70 < 1.72 ± ± ± a3128 003 0.14 0.04 < 0.16 < 0.46 < 0.64 < 0.75 ± a3128 004 1.51 0.09 0.98 0.09 0.80 0.18 0.94 0.31 < 1.61 ± ± ± ± a3128 005 0.73 0.05 0.50 0.06 0.45 0.14 < 0.85 < 2.03 ± ± ± a3128 006 0.49 0.04 0.37 0.05 < 0.42 0.86 0.22 1.90 0.60 ± ± ± ± a3128 007 0.23 0.04 < 0.18 < 0.55 < 0.77 < 1.85 ± a3128 008 2.70 0.16 1.75 0.11 1.34 0.28 1.24 0.25 < 1.67 ± ± ± ±
54 a3128 009 1.44 0.09 — 0.70 0.18 — < 1.82 ± ± a3128 010 0.20 0.05 — < 0.66 — 3.25 0.86 ± ± a3128 011 < 280900.00 < 0.12 < 0.38 < 0.58 — a3128 012 0.98 0.07 0.78 0.06 0.90 0.23 1.18 0.20 4.97 1.09 ± ± ± ± ± a3128 013 0.96 0.06 0.63 0.06 0.46 0.15 0.75 0.19 < 1.80 ± ± ± ± a3128 014 0.56 0.05 0.35 0.05 < 0.55 < 0.53 — ± ± a3128 015 < 280900.00 < 0.11 < 0.55 < 0.49 < 0.65 a3128 016 3.96 0.23 2.42 0.14 1.92 0.29 2.11 0.24 < 1.39 ± ± ± ± a3128 017 1.99 0.12 1.22 0.09 0.87 0.27 < 0.58 < 1.20 ± ± ± a3128 018 0.80 0.06 0.56 0.06 1.00 0.16 5.51 0.38 6.23 1.37 ± ± ± ± ± a3128 019 2.12 0.12 1.31 0.09 0.93 0.18 < 0.53 < 1.75 ± ± ± (continued) Table 2.4. MIR Cluster Member Photometry Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν (5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
a3128 020 1.51 0.10 — 1.01 0.27 — < 2.49 ± ± a3128 021 0.41 0.05 0.26 0.05 < 0.60 < 0.53 — ± ± a3128 023 0.23 0.04 0.17 0.04 < 0.50 < 0.49 — ± ± a3128 024 0.22 0.03 0.14 0.04 < 0.50 < 0.44 — ± ± a3128 025 11.79 0.67 6.84 0.41 5.06 0.41 3.03 0.56 < 1.70 ± ± ± ± a3128 026 0.78 0.06 0.47 0.05 < 0.60 < 0.53 < 0.72 ± ± a3128 027 0.51 0.04 0.34 0.04 < 0.50 < 0.49 < 1.22 ± ± 55 a3128 028 1.13 0.08 0.76 0.08 < 0.73 1.92 0.35 2.94 0.95 ± ± ± ± a3128 029 14.23 0.81 8.67 0.50 6.32 0.69 3.77 0.51 < 1.58 ± ± ± ± a3128 032 0.71 0.05 0.44 0.05 < 0.38 < 0.58 < 1.96 ± ± a3128 033 4.56 0.27 2.70 0.17 2.05 0.36 1.17 0.35 — ± ± ± ± a3128 034 0.49 0.05 0.33 0.06 < 0.46 < 0.64 < 1.67 ± ± a3128 035 1.31 0.08 0.86 0.07 0.82 0.17 2.09 0.26 2.55 0.82 ± ± ± ± ± a3128 036 0.13 0.04 < 0.12 < 0.60 < 0.53 < 1.80 ± a3128 037 8.77 0.50 5.09 0.31 3.85 0.42 2.32 0.44 < 1.72 ± ± ± ± a3128 038 1.70 0.11 — 1.09 0.21 — < 3.10 ± ± a3128 039 — < 0.18 — < 0.77 — a3128 040 1.14 0.08 — < 0.55 — < 2.60 ± a3128 041 0.58 0.05 — < 0.60 — < 2.53 ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν(5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
a3128 042 2.20 0.13 1.30 0.09 1.02 0.19 1.02 0.23 < 1.64 ± ± ± ± a3128 043 0.58 0.05 — < 0.60 — 3.72 1.17 ± ± a3128 044 8.54 0.49 5.46 0.32 3.78 0.40 3.30 0.33 2.85 0.85 ± ± ± ± ± a3128 045 — — — — < 1.96 a3128 046 — — < 0.73 — < 3.63 a3128 047 1.38 0.09 — < 0.60 — < 2.56 ± a3128 048 0.40 0.05 0.24 0.04 < 0.66 < 0.58 < 1.64 ± ± 56 a3128 049 1.40 0.10 0.86 0.08 < 0.73 < 0.77 < 0.86 ± ± a3128 050 3.06 0.19 2.00 0.13 — 3.36 0.35 2.91 0.98 ± ± ± ± a3128 051 — — — — < 2.80 a3128 053 — — < 0.55 — — a3128 054 < 280900.00 — < 0.46 — < 1.29 a3128 055 < 280900.00 < 0.12 < 0.50 < 0.53 < 1.69 a3128 056 0.90 0.07 0.63 0.06 1.19 0.24 7.08 0.46 8.94 1.83 ± ± ± ± ± a3128 057 4.20 0.25 — 1.84 0.29 — < 2.42 ± ± a3128 060 < 280900.00 < 0.12 < 0.66 < 0.53 — a3128 063 6.36 0.37 4.74 0.28 3.71 0.49 3.80 0.40 4.32 1.14 ± ± ± ± ± a3128 064 — — < 0.55 — < 1.40 a3128 065 10.11 0.58 6.51 0.38 4.71 0.62 3.18 0.45 < 1.66 ± ± ± ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν(5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
a3128 067 0.19 0.05 < 0.12 < 0.73 < 0.58 — ± a3128 068 < 280900.00 — < 0.55 — < 1.17 a3128 069 0.98 0.07 0.60 0.07 < 0.46 < 0.64 < 2.44 ± ± a3128 070 — < 0.14 — < 0.53 < 2.42 a3128 071 0.77 0.06 0.48 0.05 < 0.55 < 0.49 < 1.67 ± ± a3128 072 0.72 0.07 0.49 0.05 < 0.73 < 0.58 < 2.11 ± ± a3128 073 1.71 0.11 1.14 0.09 — 0.88 0.28 < 3.16 ± ± ± 57 a3128 074 < 280900.00 < 0.12 < 0.66 < 0.58 — a3128 077 0.25 0.05 0.16 0.04 < 0.66 < 0.58 — ± ± a3128 078 — — — — < 1.87 a3128 079 — — — — < 2.56 a3128 080 — 0.38 0.05 — < 0.58 < 2.09 ± a3128 081 0.33 0.06 — — — < 2.44 ± a3128 082 — 0.64 0.08 < 0.73 < 0.85 — ± a3128 085 1.22 0.08 0.79 0.08 0.49 0.17 < 0.77 < 0.96 ± ± ± a3128 087 3.16 0.18 1.94 0.14 1.42 0.21 — < 2.40 ± ± ± a3128 092 0.50 0.05 0.37 0.06 < 0.66 < 0.85 < 1.75 ± ± a3128 095 0.56 0.06 0.37 0.05 < 0.66 < 0.64 < 2.65 ± ± a3128 098 0.40 0.04 0.27 0.06 0.45 0.15 1.81 0.29 3.78 1.03 ± ± ± ± ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν(5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
a3128 099 5.00 0.29 3.13 0.21 2.57 0.33 3.08 0.55 < 2.07 ± ± ± ± a3128 101 0.82 0.07 0.55 0.07 < 0.66 2.08 0.34 2.28 0.77 ± ± ± ± a3128 102 1.83 0.12 — 0.91 0.25 — < 3.31 ± ± a3128 107 — — — — < 2.33 a3128 111 0.19 0.04 — < 0.50 — < 3.59 ± a3128 118 1.86 0.12 1.21 0.10 0.95 0.29 1.27 0.34 — ± ± ± ± a3125 001 — 1.37 0.14 — 9.49 0.85 8.68 2.28 ± ± ± 58 a3125 005 — — — — < 2.31 a3125 008 — — — — < 2.35 a3125 011 3.22 0.29 2.00 0.18 1.31 0.33 0.86 0.28 < 1.53 ± ± ± ± a3125 012 < 280900.00 < 0.16 < 0.60 < 0.77 < 1.74 a3125 013 5.15 0.44 3.30 0.28 2.27 0.34 1.51 0.28 < 1.55 ± ± ± ± a3125 014 2.58 0.23 1.62 0.15 1.10 0.28 < 0.64 < 1.42 ± ± ± a3125 015 1.39 0.14 0.89 0.10 < 0.66 < 0.58 < 1.53 ± ± a3125 016 — — — — < 3.34 a3125 017 6.47 0.55 4.00 0.35 2.86 0.44 1.84 0.40 < 1.40 ± ± ± ± a3125 018 < 280900.00 < 0.16 < 0.55 < 0.77 < 0.90 a3125 021 — — — — — a3125 023 0.45 0.06 0.28 0.07 < 0.66 < 0.77 < 0.78 ± ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν(5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
a3125 024 1.72 0.16 1.09 0.12 0.88 0.28 1.57 0.36 < 1.67 ± ± ± ± a3125 028 — — — — < 1.75 a3125 029 0.67 0.07 0.44 0.06 < 0.50 < 0.58 < 1.38 ± ± a3125 030 < 280900.00 < 0.20 < 0.60 < 0.85 — a3125 031 3.39 0.29 2.22 0.21 1.79 0.37 3.54 0.51 3.43 1.15 ± ± ± ± ± a3125 032 — — — — — a3125 034 — — — — < 2.63
59 a3125 038 — — — — < 1.22 a3125 039 < 280900.00 < 0.18 < 0.60 < 0.77 < 0.93 a3125 040 — — — — < 2.75 a3125 044 3.27 0.29 2.28 0.22 2.24 0.45 4.81 0.61 5.71 1.48 ± ± ± ± ± a3125 045 7.25 0.60 4.52 0.39 3.17 0.36 2.15 0.44 < 1.63 ± ± ± ± a644 005 — — — — — a644 011 2.61 0.21 2.55 0.24 3.62 0.48 5.15 0.62 — ± ± ± ± a644 012 < 280900.00 < 0.20 < 0.73 < 1.02 — a644 013 < 280900.00 < 0.16 < 0.66 < 0.93 — a644 017 — — — — — a644 020 — — — — — a644 024 2.40 0.21 1.69 0.17 1.32 0.37 1.80 0.46 — ± ± ± ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν(5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
a644 025 — — — — — a2104 001 0.18 0.02 0.13 0.01 < 0.08 < 0.19 < 0.07 ± ± a2104 002 0.98 0.09 0.70 0.07 0.45 0.08 < 0.19 — ± ± ± a2104 003 3.05 0.28 2.24 0.20 1.30 0.15 0.89 0.19 0.96 0.30 ± ± ± ± ± a2104 004 0.34 0.03 0.25 0.02 < 0.09 < 0.16 — ± ± a2104 005 0.72 0.07 0.53 0.05 0.31 0.05 < 0.16 — ± ± ± a2104 006 0.17 0.02 0.12 0.01 < 0.09 < 0.16 — ± ± 60 a2104 007 0.56 0.05 0.42 0.04 0.24 0.03 < 0.16 < 0.24 ± ± ± a2104 008 0.80 0.07 0.55 0.05 0.34 0.07 < 0.16 < 0.24 ± ± ± a2104 009 0.34 0.03 0.24 0.02 0.13 0.03 < 0.21 < 0.18 ± ± ± a2104 010 — — — < 0.40 < 0.29 a2104 011 0.16 0.02 0.11 0.01 < 0.10 < 0.18 — ± ± a2104 012 — — — < 0.40 — a2104 013 1.85 0.17 1.30 0.12 0.80 0.10 0.56 0.12 < 0.26 ± ± ± ± a2104 014 0.72 0.07 0.52 0.05 0.30 0.04 0.22 0.06 < 0.22 ± ± ± ± a2104 015 0.34 0.03 0.23 0.02 0.18 0.03 0.50 0.07 0.42 0.13 ± ± ± ± ± a2104 016 0.14 0.01 0.10 0.01 < 0.06 < 0.15 < 0.16 ± ± a2104 017 1.25 0.11 0.89 0.08 0.54 0.07 0.35 0.08 — ± ± ± ± a2104 018 0.51 0.05 0.38 0.03 0.23 0.03 0.17 0.05 < 0.22 ± ± ± ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν(5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
a2104 019 0.80 0.07 0.55 0.05 0.35 0.05 0.32 0.08 < 0.29 ± ± ± ± a2104 020 0.71 0.07 0.49 0.05 0.29 0.04 < 0.19 < 0.18 ± ± ± a2104 021 0.29 0.03 0.21 0.02 0.13 0.03 < 0.15 < 0.22 ± ± ± a2104 022 0.35 0.03 0.26 0.03 0.23 0.05 1.07 0.13 1.63 0.40 ± ± ± ± ± a2104 023 0.64 0.06 0.45 0.04 0.27 0.04 0.17 0.06 < 0.24 ± ± ± ± a2104 024 0.13 0.01 0.10 0.01 0.08 0.02 < 0.15 < 0.18 ± ± ± a2104 025 0.67 0.06 0.49 0.04 0.28 0.03 0.20 0.06 < 0.18 ± ± ± ± 61 a2104 026 — — — < 0.49 < 0.38 a2104 027 0.76 0.07 0.53 0.05 0.33 0.06 0.23 0.07 < 0.26 ± ± ± ± a2104 028 0.37 0.03 0.27 0.02 0.15 0.04 < 0.13 — ± ± ± a2104 029 0.10 0.01 0.07 0.01 < 0.05 < 0.13 — ± ± a2104 030 0.41 0.04 0.29 0.03 0.16 0.04 < 0.13 — ± ± ± a2104 031 0.23 0.02 0.16 0.02 0.10 0.03 < 0.12 — ± ± ± a2104 032 2.02 0.18 1.47 0.13 0.87 0.09 0.59 0.11 < 0.20 ± ± ± ± a2104 033 0.13 0.01 0.09 0.01 < 0.09 < 0.21 < 0.06 ± ± a2104 034 0.78 0.07 0.54 0.05 0.33 0.06 < 0.18 < 0.26 ± ± ± a2104 035 0.95 0.09 0.69 0.06 0.43 0.06 0.72 0.10 0.86 0.23 ± ± ± ± ± a2104 036 0.12 0.01 0.08 0.01 < 0.14 < 0.31 < 0.15 ± ± a2104 037 0.13 0.01 0.09 0.01 < 0.14 < 0.31 < 0.14 ± ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν(5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
a2104 038 0.23 0.02 0.17 0.02 0.10 0.02 0.18 0.06 0.32 0.10 ± ± ± ± ± a2104 039 0.14 0.01 — < 0.10 — 0.65 0.20 ± ± a2104 040 0.90 0.08 0.65 0.06 0.41 0.06 0.40 0.07 0.46 0.15 ± ± ± ± ± a2104 041 0.12 0.01 0.08 0.01 < 0.10 0.34 0.06 < 0.29 ± ± ± a2104 042 0.31 0.03 0.23 0.02 0.12 0.04 < 0.21 < 0.38 ± ± ± a2104 043 0.61 0.06 0.43 0.04 0.25 0.04 < 0.21 — ± ± ± a2104 044 0.13 0.01 0.10 0.01 < 0.09 < 0.15 < 0.26 ± ± 62 a2104 045 0.55 0.05 0.40 0.04 0.27 0.03 0.71 0.09 0.71 0.18 ± ± ± ± ± a2104 046 0.12 0.01 0.09 0.01 < 0.09 < 0.21 — ± ± a2104 047 0.96 0.09 0.76 0.07 0.60 0.06 2.63 0.26 3.05 0.72 ± ± ± ± ± a2104 048 0.16 0.02 0.11 0.01 < 0.10 < 0.18 < 0.24 ± ± a2104 049 0.47 0.04 0.34 0.03 0.19 0.06 < 0.37 < 0.41 ± ± ± a2104 050 0.30 0.03 0.22 0.02 0.13 0.03 < 0.18 — ± ± ± a2104 051 0.46 0.04 0.37 0.03 0.32 0.03 0.40 0.06 0.91 0.22 ± ± ± ± ± a2104 052 0.34 0.03 0.29 0.03 0.18 0.04 0.66 0.11 1.52 0.36 ± ± ± ± ± a2104 053 — — — — < 0.45 a2104 054 0.38 0.04 0.28 0.03 0.16 0.05 < 0.13 — ± ± ± a2104 055 0.39 0.04 0.28 0.03 0.16 0.04 < 0.16 < 0.26 ± ± ± a2104 056 0.15 0.01 — < 0.13 — — ± (continued) Table 2.4—Continued
Name Fν (3.6 m) Fν (4.5 m) Fν(5.8 m) Fν(8.0 m) Fν (24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
a2104 057 0.47 0.04 0.33 0.03 0.19 0.04 < 0.13 — ± ± ± a2104 059 0.47 0.04 0.33 0.03 0.19 0.05 < 0.21 < 0.24 ± ± ± a2104 061 0.10 0.01 0.07 0.01 < 0.09 0.63 0.10 0.64 0.16 ± ± ± ± a2104 062 0.51 0.05 0.36 0.04 0.24 0.04 0.68 0.14 0.52 0.16 ± ± ± ± ± a2104 063 0.14 0.01 0.11 0.01 < 0.09 < 0.21 < 0.24 ± ± a2104 064 — 0.96 0.09 — 0.66 0.12 < 0.29 ± ± a2104 065 0.11 0.01 0.07 0.01 < 0.06 < 0.15 — ± ± 63 a2104 069 0.41 0.04 0.29 0.03 0.20 0.05 < 0.23 < 0.50 ± ± ± a2104 070 — — — — — a2104 072 — — — < 0.44 < 0.54 a2104 073 0.12 0.01 — < 0.10 — 1.32 0.32 ± ± a2104 074 1.41 0.13 1.03 0.09 0.71 0.07 2.06 0.21 2.53 0.60 ± ± ± ± ± a2104 075 6.49 0.59 7.92 0.72 10.09 0.92 12.91 1.18 38.05 8.86 ± ± ± ± ± a2104 076 — 0.87 0.08 0.53 0.11 0.41 0.10 — ± ± ± a2104 077 0.77 0.07 0.55 0.06 0.39 0.05 1.05 0.22 0.87 0.25 ± ± ± ± ± a2104 079 — 0.86 0.08 — 2.61 0.27 6.77 1.59 ± ± ± a1689 004 2.89 0.26 2.11 0.19 1.21 0.11 0.87 0.08 < 0.29 ± ± ± ± a1689 008 0.78 0.07 0.57 0.05 0.33 0.03 0.23 0.03 < 0.26 ± ± ± ± a1689 012 0.53 0.05 0.40 0.04 0.27 0.03 0.76 0.07 1.19 0.35 ± ± ± ± ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν(5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
a1689 014 0.65 0.06 0.49 0.04 0.29 0.03 0.23 0.02 < 0.29 ± ± ± ± a1689 015 1.89 0.17 1.42 0.13 0.82 0.07 0.59 0.05 < 0.29 ± ± ± ± a1689 021 1.04 0.09 0.76 0.07 0.44 0.04 0.30 0.04 < 0.11 ± ± ± ± a1689 022 0.53 0.05 0.40 0.04 0.23 0.02 0.19 0.02 < 0.31 ± ± ± ± a1689 023 0.73 0.06 0.55 0.05 0.32 0.03 0.25 0.04 — ± ± ± ± a1689 026 0.63 0.06 0.47 0.04 0.28 0.03 0.22 0.02 < 0.31 ± ± ± ± a1689 027 2.02 0.18 1.50 0.13 0.88 0.08 0.61 0.06 < 0.31 ± ± ± ± 64 a1689 030 1.54 0.14 1.13 0.10 0.67 0.06 0.51 0.05 < 0.34 ± ± ± ± a1689 031 0.31 0.03 0.24 0.02 0.14 0.01 0.11 0.01 < 0.29 ± ± ± ± a1689 036 0.23 0.02 0.17 0.01 0.10 0.01 0.07 0.01 < 0.29 ± ± ± ± a1689 038 0.78 0.07 0.58 0.05 0.33 0.03 0.24 0.03 — ± ± ± ± a1689 039 0.19 0.02 0.14 0.01 0.09 0.01 0.07 0.01 < 0.31 ± ± ± ± a1689 041 0.11 0.01 0.08 0.01 0.05 0.01 0.03 0.01 — ± ± ± ± a1689 045 0.79 0.07 0.60 0.05 0.36 0.03 0.46 0.04 0.65 0.22 ± ± ± ± ± a1689 049 0.09 0.01 0.07 0.01 0.04 0.00 0.03 0.01 — ± ± ± ± a1689 050 0.14 0.01 0.10 0.01 0.06 0.01 0.04 0.01 < 0.31 ± ± ± ± a1689 052 0.33 0.03 0.25 0.02 0.18 0.02 0.48 0.04 1.25 0.31 ± ± ± ± ± a1689 055 0.26 0.02 0.19 0.02 0.11 0.01 0.09 0.01 < 0.29 ± ± ± ± a1689 058 0.37 0.03 0.28 0.02 0.16 0.01 0.12 0.02 < 0.31 ± ± ± ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν(5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
a1689 059 0.25 0.02 0.23 0.02 0.23 0.02 0.29 0.03 1.21 0.31 ± ± ± ± ± a1689 060 0.18 0.02 0.12 0.01 0.08 0.01 0.05 0.01 < 0.31 ± ± ± ± a1689 061 0.02 0.00 0.01 0.00 < 0.01 < 0.02 < 0.31 ± ± a1689 062 0.17 0.01 0.14 0.01 0.08 0.01 0.06 0.01 — ± ± ± ± a1689 064 0.18 0.02 0.17 0.01 0.10 0.01 0.07 0.01 — ± ± ± ± a1689 065 0.17 0.01 0.14 0.01 0.08 0.01 0.06 0.01 < 0.11 ± ± ± ± a1689 067 0.05 0.00 0.04 0.00 0.03 0.00 0.03 0.01 < 0.31 ± ± ± ± 65 a1689 069 0.23 0.02 0.18 0.02 0.10 0.01 0.07 0.01 < 0.29 ± ± ± ± a1689 070 0.27 0.02 0.20 0.02 0.13 0.01 0.09 0.01 — ± ± ± ± a1689 071 0.42 0.04 0.33 0.03 0.25 0.02 0.60 0.05 2.06 0.49 ± ± ± ± ± a1689 072 0.11 0.01 0.08 0.01 0.05 0.01 0.03 0.01 — ± ± ± ± a1689 074 0.12 0.01 0.09 0.01 0.07 0.01 0.25 0.02 0.44 0.15 ± ± ± ± ± a1689 076 0.41 0.04 0.31 0.03 0.17 0.02 0.13 0.02 < 0.31 ± ± ± ± a1689 077 0.30 0.03 0.22 0.02 0.14 0.01 0.09 0.01 < 0.31 ± ± ± ± a1689 078 0.15 0.01 0.12 0.01 0.07 0.01 0.05 0.01 < 0.31 ± ± ± ± a1689 079 0.45 0.04 0.34 0.03 0.19 0.02 0.16 0.02 < 0.31 ± ± ± ± a1689 080 0.30 0.03 0.23 0.02 0.13 0.01 0.11 0.01 — ± ± ± ± a1689 083 0.33 0.03 0.25 0.02 0.14 0.01 0.10 0.01 < 0.31 ± ± ± ± a1689 085 0.08 0.01 0.07 0.01 0.05 0.01 0.03 0.01 — ± ± ± ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν(5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
a1689 086 0.07 0.01 0.05 0.00 0.03 0.00 < 0.03 — ± ± ± a1689 087 0.03 0.00 0.03 0.00 0.02 0.00 0.13 0.01 < 0.31 ± ± ± ± a1689 088 0.04 0.00 0.03 0.00 0.02 0.00 0.06 0.01 < 0.31 ± ± ± ± a1689 092 0.04 0.00 0.03 0.00 0.02 0.00 < 0.03 — ± ± ± a1689 093 0.21 0.02 0.16 0.01 0.09 0.01 0.07 0.01 — ± ± ± ± a1689 094 0.06 0.01 0.06 0.01 — 0.11 0.01 0.99 0.25 ± ± ± ± a1689 095 0.54 0.05 0.40 0.04 0.25 0.02 0.17 0.02 < 0.31 ± ± ± ± 66 a1689 096 0.29 0.03 0.23 0.02 0.16 0.01 0.48 0.04 0.58 0.17 ± ± ± ± ± a1689 097 0.05 0.00 0.04 0.00 0.02 0.00 < 0.03 — ± ± ± a1689 099 0.06 0.00 0.04 0.00 0.04 0.00 0.03 0.01 — ± ± ± ± a1689 100 0.01 0.00 0.01 0.00 < 0.01 < 0.03 < 0.11 ± ± a1689 103 0.52 0.05 0.43 0.04 0.33 0.03 1.18 0.11 5.03 1.18 ± ± ± ± ± a1689 105 0.24 0.02 0.19 0.02 0.10 0.01 0.08 0.01 < 0.31 ± ± ± ± a1689 106 0.10 0.01 0.07 0.01 — 0.28 0.03 0.98 0.25 ± ± ± ± a1689 107 0.01 0.00 < 0.00 < 0.01 < 0.02 < 0.31 ± a1689 109 0.08 0.01 0.13 0.01 0.19 0.02 0.30 0.03 1.00 0.26 ± ± ± ± ± a1689 110 0.03 0.00 0.02 0.00 0.01 0.00 0.03 0.01 < 0.31 ± ± ± ± a1689 111 0.01 0.00 0.01 0.00 < 0.01 < 0.03 — ± ± a1689 112 0.19 0.02 0.17 0.02 0.16 0.01 1.35 0.12 2.97 0.70 ± ± ± ± ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν(5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
a1689 113 0.06 0.01 0.04 0.00 0.03 0.01 < 0.03 < 0.31 ± ± ± a1689 114 0.18 0.02 0.14 0.01 0.09 0.01 0.07 0.01 < 0.31 ± ± ± ± a1689 115 0.04 0.00 0.03 0.00 0.03 0.00 0.04 0.01 — ± ± ± ± a1689 117 0.24 0.02 0.18 0.02 0.11 0.01 0.07 0.02 < 0.50 ± ± ± ± a1689 118 0.58 0.05 0.45 0.04 0.28 0.03 0.34 0.04 < 0.54 ± ± ± ± a1689 119 0.40 0.04 0.30 0.03 0.17 0.02 0.12 0.02 < 0.34 ± ± ± ± a1689 120 0.25 0.02 0.19 0.02 0.11 0.01 0.07 0.02 < 0.20 ± ± ± ± 67 a1689 121 0.07 0.01 0.06 0.01 — < 0.03 — ± ± a1689 122 0.22 0.02 0.16 0.01 0.10 0.01 0.07 0.02 < 0.20 ± ± ± ± a1689 123 0.02 0.00 0.01 0.00 < 0.02 < 0.03 < 1.23 ± ± a1689 124 0.75 0.07 0.54 0.05 0.32 0.03 0.21 0.03 < 0.45 ± ± ± ± a1689 126 0.01 0.00 0.01 0.00 < 0.01 < 0.03 — ± ± a1689 127 0.04 0.00 0.03 0.00 0.02 0.00 < 0.03 < 0.31 ± ± ± a1689 128 0.10 0.01 0.08 0.01 0.05 0.01 < 0.05 — ± ± ± a1689 129 0.14 0.01 0.11 0.01 0.06 0.01 0.04 0.01 — ± ± ± ± a1689 130 0.20 0.02 0.15 0.01 0.10 0.01 0.26 0.03 < 0.50 ± ± ± ± a1689 131 0.01 0.00 < 0.01 < 0.03 < 0.06 < 0.54 ± a1689 132 0.14 0.01 0.11 0.01 0.09 0.01 0.05 0.01 — ± ± ± ± a1689 135 0.02 0.00 0.02 0.00 < 0.02 < 0.04 — ± ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν(5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
a1689 136 0.58 0.05 0.42 0.04 0.24 0.02 0.18 0.04 < 0.50 ± ± ± ± a1689 138 0.29 0.03 0.22 0.02 0.14 0.02 0.17 0.03 — ± ± ± ± a1689 139 0.34 0.03 0.26 0.02 0.16 0.02 0.10 0.03 — ± ± ± ± a1689 140 0.21 0.02 0.16 0.01 0.14 0.02 < 0.10 — ± ± ± a1689 141 0.32 0.03 0.23 0.02 0.21 0.03 < 0.11 — ± ± ± a1689 142 0.25 0.02 0.20 0.02 0.18 0.02 1.28 0.12 1.63 0.43 ± ± ± ± ± a1689 143 0.83 0.07 0.61 0.05 0.37 0.03 0.25 0.03 — ± ± ± ± 68 a1689 144 0.45 0.04 0.34 0.03 0.19 0.02 0.14 0.02 < 0.54 ± ± ± ± a1689 145 0.01 0.00 0.01 0.00 < 0.02 < 0.07 < 0.54 ± ± a1689 147 0.26 0.02 0.19 0.02 0.11 0.01 < 0.13 < 0.50 ± ± ± a1689 148 0.01 0.00 0.01 0.00 < 0.02 < 0.06 — ± ± a1689 149 0.55 0.05 0.43 0.04 0.23 0.02 0.24 0.03 < 0.45 ± ± ± ± a1689 150 0.28 0.02 0.22 0.02 0.12 0.01 0.11 0.02 < 0.54 ± ± ± ± a1689 151 0.24 0.02 0.19 0.02 0.12 0.01 0.22 0.06 < 0.54 ± ± ± ± a1689 153 0.13 0.01 0.10 0.01 0.10 0.01 0.09 0.02 < 0.77 ± ± ± ± a1689 155 0.02 0.00 0.01 0.00 < 0.04 < 0.05 < 0.54 ± ± a1689 156 0.01 0.00 < 0.01 < 0.03 < 0.08 < 0.73 ± a1689 158 0.61 0.06 0.46 0.04 0.26 0.04 < 0.19 — ± ± ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν(5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
a1689 159 < 280900.00 < 0.02 < 0.05 < 0.15 — a1689 160 0.53 0.05 0.41 0.04 0.27 0.03 < 0.19 — ± ± ± a1689 161 0.25 0.02 0.21 0.02 0.13 0.02 0.09 0.03 — ± ± ± ± a1689 162 0.08 0.01 0.05 0.01 < 0.07 < 0.18 — ± ± a1689 163 0.06 0.01 0.05 0.01 < 0.05 — — ± ± a1689 164 0.28 0.03 0.22 0.02 0.17 0.02 0.76 0.09 1.37 0.37 ± ± ± ± ± a1689 165 0.10 0.01 0.08 0.01 0.04 0.01 < 0.06 — ± ± ± 69 a1689 166 0.08 0.01 0.06 0.01 < 0.05 < 0.19 — ± ± a1689 167 0.49 0.04 0.34 0.03 0.21 0.02 < 0.18 < 0.38 ± ± ± a1689 168 0.79 0.07 0.59 0.05 0.34 0.04 0.25 0.03 < 0.54 ± ± ± ± a1689 170 0.14 0.01 0.11 0.01 0.06 0.01 < 0.12 < 0.31 ± ± ± a1689 171 0.03 0.00 0.02 0.00 < 0.03 < 0.07 — ± ± a1689 172 0.08 0.01 0.06 0.01 0.04 0.01 < 0.18 < 0.54 ± ± ± a1689 173 0.02 0.00 < 0.01 < 0.03 < 0.10 — ± a1689 174 — 0.01 0.00 < 0.10 < 0.03 < 0.54 ± a1689 175 0.01 0.00 — < 0.02 — — ± a1689 177 — 0.07 0.01 < 0.05 0.08 0.01 < 0.54 ± ± a1689 178 — < 0.00 — < 0.02 < 0.54 a1689 179 0.05 0.01 0.04 0.00 < 0.08 < 0.10 < 0.38 ± ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν(5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
a1689 181 — 0.11 0.01 — 0.07 0.01 < 0.54 ± ± a1689 185 0.03 0.00 — < 0.02 — — ± a1689 186 — 0.72 0.07 0.47 0.06 < 0.19 — ± ± a1689 187 — 0.06 0.01 — 0.30 0.03 < 0.60 ± ± a1689 189 0.15 0.02 0.15 0.02 0.12 0.03 < 0.19 — ± ± ± a1689 191 — — — < 0.21 — a1689 192 0.07 0.01 0.05 0.01 < 0.07 < 0.19 — ± ± 70 a1689 194 — — — — — a1689 195 0.19 0.02 0.14 0.02 0.12 0.03 0.47 0.08 — ± ± ± ± a1689 196 — 0.07 0.01 — < 0.08 — ± a1689 198 0.02 0.00 — < 0.04 — < 0.34 ± a1689 200 — — — < 0.19 — a1689 201 — — — < 0.19 — a1689 204 — 0.06 0.01 — 0.28 0.05 — ± ± a1689 207 — 0.02 0.00 — 0.06 0.02 — ± ± a1689 209 0.53 0.05 0.37 0.04 0.23 0.04 < 0.19 — ± ± ± a1689 211 — — — < 0.28 — a1689 215 0.04 0.01 0.02 0.01 < 0.07 < 0.19 — ± ± a1689 217 0.75 0.07 — 0.49 0.04 — 3.98 0.92 ± ± ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν(5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
a1689 218 0.27 0.03 0.18 0.02 0.15 0.03 0.67 0.10 — ± ± ± ± a1689 219 0.16 0.02 0.13 0.01 0.09 0.03 < 0.19 — ± ± ± a1689 220 0.02 0.00 — — < 0.21 — ± a1689 221 0.09 0.01 — 0.04 0.01 — < 0.18 ± ± a1689 229 — — — < 0.23 — a1689 231 0.06 0.01 — — < 0.21 — ± a1689 233 0.27 0.03— — — — ± 71 a1689 234 — — — — — a1689 238 0.64 0.06 — 0.30 0.03 — 0.67 0.17 ± ± ± a1689 244 — 0.10 0.01 — 0.60 0.05 0.86 0.27 ± ± ± a1689 251 — — — — — a1689 252 — — — — 2.05 0.49 ± a2163 001 0.10 0.01 0.08 0.01 < 0.07 < 0.21 < 0.10 ± ± a2163 002 1.02 0.10 0.84 0.09 0.79 0.09 2.80 0.32 3.72 0.90 ± ± ± ± ± a2163 003 0.01 0.00 < 0.02 < 0.06 < 0.18 < 0.09 ± a2163 004 0.08 0.01 0.06 0.01 < 0.07 < 0.23 < 0.29 ± ± a2163 005 0.44 0.04 0.35 0.04 0.20 0.04 < 0.21 — ± ± ± a2163 006 0.32 0.03 — 0.16 0.04 — < 0.45 ± ± a2163 008 0.09 0.01 — 0.08 0.02 — < 0.76 ± ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν(5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
a2163 013 0.04 0.01 — < 0.07 — < 0.60 ± a2163 015 — — — — < 0.50 a2163 016 0.15 0.02 — < 0.06 — — ± a2163 019 — — — — — a2163 030 — — — — < 1.04 a2163 039 0.05 0.01 0.04 0.01 < 0.06 < 0.28 < 0.15 ± ± a2163 051 0.26 0.03 0.19 0.02 0.12 0.03 < 0.21 < 0.29 ± ± ± 72 a2163 060 — — — — — a2163 075 0.13 0.01 0.10 0.01 0.06 0.02 < 0.21 < 0.41 ± ± ± a2163 088 0.13 0.01 0.10 0.01 < 0.07 0.40 0.09 < 0.72 ± ± ± a2163 091 0.14 0.01 0.11 0.01 0.09 0.02 < 0.16 0.35 0.12 ± ± ± ± a2163 093 — — — — — a2163 094 0.26 0.03 0.19 0.02 0.14 0.04 < 0.23 < 0.31 ± ± ± a2163 096 2.43 0.25 1.87 0.19 1.04 0.14 1.01 0.29 < 0.26 ± ± ± ± a2163 097 0.07 0.01 0.05 0.01 < 0.07 < 0.21 — ± ± a2163 098 — — — — < 0.38 a2163 101 — — — — < 0.41 a2163 109 0.07 0.01 0.06 0.01 < 0.07 < 0.18 — ± ± a2163 110 — 0.10 0.01 — < 0.28 < 0.34 ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν (5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
a2163 111 — 0.30 0.03 — < 0.21 < 0.38 ± ms1008 001 0.08 0.01 0.07 0.01 0.04 0.01 < 0.05 — ± ± ± ms1008 002 0.03 0.00 0.03 0.00 < 0.02 < 0.04 — ± ± ms1008 003 0.14 0.01 0.10 0.01 0.08 0.01 < 0.05 — ± ± ± ms1008 004 2.18 0.16 1.68 0.12 1.12 0.09 0.78 0.10 — ± ± ± ± ms1008 005 0.40 0.03 0.31 0.02 0.21 0.02 0.17 0.04 — ± ± ± ± ms1008 006 0.38 0.03 0.31 0.02 0.19 0.02 0.13 0.03 — ± ± ± ± 73 ms1008 007 0.15 0.01 0.13 0.01 0.09 0.01 0.06 0.02 — ± ± ± ± ms1008 008 0.29 0.02 0.24 0.02 0.19 0.02 0.17 0.02 — ± ± ± ± ms1008 009 0.09 0.01 0.08 0.01 0.06 0.01 0.21 0.02 — ± ± ± ± ms1008 010 0.21 0.02 0.17 0.01 0.10 0.01 < 0.05 — ± ± ± ms1008 011 0.17 0.01 0.14 0.01 0.09 0.01 < 0.04 — ± ± ± ms1008 012 0.09 0.01 0.07 0.01 0.05 0.01 < 0.04 — ± ± ± ms1008 013 0.03 0.00 0.03 0.00 < 0.02 < 0.04 — ± ± ms1008 014 0.15 0.01 0.11 0.01 0.08 0.01 < 0.05 — ± ± ± ms1008 015 0.12 0.01 0.12 0.01 0.09 0.01 0.39 0.04 — ± ± ± ± ms1008 016 0.06 0.00 0.04 0.00 0.03 0.01 < 0.04 — ± ± ± ms1008 017 0.04 0.00 0.03 0.00 < 0.02 < 0.04 — ± ± ms1008 018 0.07 0.01 0.06 0.00 0.03 0.01 < 0.04 — ± ± ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν (5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
ms1008 019 0.13 0.01 0.11 0.01 0.07 0.01 < 0.04 — ± ± ± ms1008 020 0.06 0.00 0.04 0.00 0.03 0.01 < 0.04 — ± ± ± ms1008 021 0.04 0.00 0.03 0.00 < 0.02 < 0.05 — ± ± ms1008 022 0.22 0.02 0.17 0.01 0.11 0.01 0.07 0.01 — ± ± ± ± ms1008 023 0.11 0.01 0.09 0.01 0.06 0.01 < 0.04 — ± ± ± ms1008 024 0.14 0.01 0.11 0.01 0.08 0.01 0.05 0.01 — ± ± ± ± ms1008 025 0.08 0.01 0.06 0.00 0.04 0.01 < 0.04 — ± ± ± 74 ms1008 029 0.10 0.01 0.08 0.01 0.05 0.01 < 0.04 — ± ± ± ms1008 030 0.13 0.01 0.11 0.01 0.08 0.01 0.17 0.02 — ± ± ± ± ms1008 031 0.14 0.01 0.12 0.01 0.08 0.01 0.18 0.02 — ± ± ± ± ms1008 033 0.38 0.03 0.29 0.02 0.20 0.02 0.12 0.02 — ± ± ± ± ms1008 034 0.05 0.00 0.05 0.00 0.03 0.01 0.14 0.02 — ± ± ± ± ms1008 035 0.50 0.04 0.40 0.03 0.29 0.03 0.30 0.04 — ± ± ± ± ms1008 036 0.05 0.00 0.04 0.00 0.03 0.01 < 0.03 — ± ± ± ms1008 037 0.01 0.00 0.01 0.00 < 0.02 < 0.05 — ± ± ms1008 039 0.20 0.01 0.16 0.01 0.09 0.01 0.08 0.02 — ± ± ± ± ms1008 040 0.04 0.00 0.03 0.00 0.02 0.01 < 0.03 — ± ± ± ms1008 041 0.05 0.00 0.04 0.00 0.03 0.01 < 0.03 — ± ± ± ms1008 043 0.11 0.01 0.09 0.01 0.06 0.01 < 0.04 — ± ± ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν (5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
ms1008 044 0.38 0.03 0.31 0.02 0.22 0.02 0.22 0.03 — ± ± ± ± ms1008 045 0.07 0.01 0.05 0.00 0.04 0.01 < 0.03 — ± ± ± ms1008 046 0.07 0.01 0.06 0.00 0.05 0.01 0.14 0.02 — ± ± ± ± ms1008 048 0.33 0.02 0.27 0.02 0.32 0.03 0.36 0.03 — ± ± ± ± ms1008 049 0.11 0.01 0.10 0.01 0.06 0.01 < 0.05 — ± ± ± ms1008 050 0.06 0.00 0.05 0.00 0.03 0.01 < 0.05 — ± ± ± ms1008 051 0.08 0.01 0.06 0.00 0.05 0.01 0.06 0.01 — ± ± ± ± 75 ms1008 052 0.19 0.01 0.15 0.01 0.09 0.02 < 0.03 — ± ± ± ms1008 053 0.23 0.02 0.16 0.01 0.12 0.01 0.08 0.02 — ± ± ± ± ms1008 055 0.12 0.01 0.09 0.01 0.06 0.01 < 0.05 — ± ± ± ms1008 056 0.86 0.06 0.69 0.05 0.48 0.04 0.31 0.03 — ± ± ± ± ms1008 057 0.16 0.01 0.13 0.01 0.09 0.02 < 0.06 — ± ± ± ms1008 058 0.12 0.01 0.10 0.01 0.08 0.01 0.07 0.01 — ± ± ± ± ms1008 059 0.24 0.02 0.18 0.01 0.12 0.02 < 0.07 — ± ± ± ms1008 060 0.08 0.01 0.06 0.00 0.04 0.01 < 0.04 — ± ± ± ms1008 062 0.10 0.01 0.08 0.01 0.06 0.01 < 0.07 — ± ± ± ms1008 063 0.03 0.00 0.03 0.00 < 0.02 0.08 0.02 — ± ± ± ms1008 064 0.05 0.01 0.05 0.00 < 0.03 < 0.08 — ± ± ms1008 066 — — 0.19 0.02 0.20 0.06 — ± ± (continued) Table 2.4—Continued
Name Fν (3.6 m) Fν(4.5 m) Fν(5.8 m) Fν (8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
ms1008 068 0.01 0.00 0.01 0.00 < 0.03 < 0.08 — ± ± ms1008 070 0.18 0.01 0.15 0.01 0.12 0.01 < 0.06 — ± ± ± ms1008 075 0.09 0.01 0.07 0.01 0.05 0.01 < 0.04 — ± ± ± ms1008 076 0.10 0.01 0.08 0.01 0.05 0.01 < 0.05 — ± ± ± ms1008 078 0.09 0.01 0.07 0.01 0.04 0.01 < 0.05 — ± ± ± ms1008 087 — 0.06 0.01 0.04 0.01 < 0.08 — ± ± ms1008 089 0.14 0.01 0.13 0.01 0.11 0.01 0.23 0.02 — ± ± ± ± 76 ms1008 094 0.09 0.01 0.07 0.01 0.05 0.01 < 0.05 — ± ± ± ms1008 095 — 0.02 0.00 — < 0.04 — ± ms1008 096 0.06 0.00 0.04 0.00 0.03 0.01 < 0.05 — ± ± ± ac114 001 0.00 0.00 0.00 0.00 < 0.01 < 0.02 — ± ± ac114 002 0.05 0.00 0.05 0.00 0.03 0.01 0.08 0.02 0.23 0.06 ± ± ± ± ± ac114 003 0.12 0.01 0.10 0.01 0.06 0.01 < 0.03 < 0.07 ± ± ± ac114 004 0.19 0.01 0.17 0.01 0.13 0.01 0.20 0.02 0.48 0.12 ± ± ± ± ± ac114 005 0.02 0.00 0.02 0.00 < 0.01 < 0.03 — ± ± ac114 006 0.19 0.01 0.17 0.01 0.11 0.01 0.12 0.02 — ± ± ± ± ac114 007 0.00 0.00 < 0.00 < 0.01 < 0.03 < 0.09 ± ac114 008 0.08 0.01 0.07 0.01 0.04 0.01 < 0.03 — ± ± ± ac114 009 0.04 0.00 0.03 0.00 0.02 0.00 < 0.03 — ± ± ± (continued) Table 2.4—Continued
Name Fν (3.6 m) Fν(4.5 m) Fν (5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
ac114 010 0.05 0.00 0.04 0.00 0.03 0.00 < 0.03 — ± ± ± ac114 011 0.02 0.00 0.01 0.00 < 0.01 < 0.03 — ± ± ac114 012 0.05 0.00 0.05 0.00 0.04 0.01 < 0.03 < 0.08 ± ± ± ac114 014 0.01 0.00 0.01 0.00 < 0.01 < 0.03 — ± ± ac114 015 0.03 0.00 — — < 0.03 — ± ac114 016 0.27 0.02 0.21 0.02 0.13 0.01 0.09 0.02 < 0.07 ± ± ± ± ac114 017 0.07 0.01 0.06 0.00 0.04 0.01 < 0.04 < 0.10 ± ± ± 77 ac114 018 0.14 0.01 0.11 0.01 0.07 0.01 0.05 0.01 < 0.07 ± ± ± ± ac114 019 0.01 0.00 0.01 0.00 < 0.02 < 0.03 — ± ± ac114 020 0.24 0.02 0.20 0.01 0.12 0.01 0.08 0.01 — ± ± ± ± ac114 021 0.16 0.01 0.14 0.01 0.10 0.01 0.18 0.02 0.72 0.16 ± ± ± ± ± ac114 022 0.03 0.00 0.02 0.00 < 0.01 < 0.03 — ± ± ac114 023 0.07 0.01 0.07 0.01 0.04 0.01 0.05 0.01 0.13 0.04 ± ± ± ± ± ac114 025 0.03 0.00 0.03 0.00 0.02 0.01 < 0.03 < 0.08 ± ± ± ac114 026 0.17 0.01 0.13 0.01 0.09 0.01 0.07 0.01 < 0.10 ± ± ± ± ac114 028 0.05 0.00 0.04 0.00 0.03 0.00 0.06 0.01 0.15 0.04 ± ± ± ± ± ac114 029 0.04 0.00 0.03 0.00 0.02 0.00 < 0.03 < 0.03 ± ± ± ac114 030 0.12 0.01 0.10 0.01 0.06 0.01 0.04 0.01 < 0.07 ± ± ± ± ac114 031 0.01 0.00 0.01 0.00 < 0.01 < 0.03 < 0.07 ± ± (continued) Table 2.4—Continued
Name Fν (3.6 m) Fν(4.5 m) Fν (5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
ac114 032 0.01 0.00 0.01 0.00 < 0.02 < 0.04 — ± ± ac114 033 1.70 0.12 1.39 0.10 0.90 0.07 0.62 0.06 0.48 0.14 ± ± ± ± ± ac114 035 0.10 0.01 0.08 0.01 0.05 0.01 0.04 0.01 < 0.07 ± ± ± ± ac114 036 0.15 0.01 0.13 0.01 0.08 0.01 0.12 0.02 0.25 0.08 ± ± ± ± ± ac114 037 0.01 0.00 < 0.01 < 0.03 < 0.05 < 0.12 ± ac114 038 0.08 0.01 0.06 0.00 0.04 0.01 < 0.03 — ± ± ± ac114 039 0.03 0.00 0.02 0.00 < 0.02 < 0.03 — ± ± 78 ac114 040 0.10 0.01 0.09 0.01 0.05 0.01 0.04 0.01 < 0.07 ± ± ± ± ac114 041 0.33 0.02 0.27 0.02 0.18 0.01 0.13 0.02 < 0.09 ± ± ± ± ac114 042 0.21 0.02 0.17 0.01 0.12 0.01 0.09 0.01 < 0.08 ± ± ± ± ac114 043 0.06 0.01 0.05 0.00 0.03 0.01 < 0.03 — ± ± ± ac114 044 0.01 0.00 0.00 0.00 < 0.01 — — ± ± ac114 045 0.11 0.01 0.09 0.01 0.06 0.01 < 0.03 — ± ± ± ac114 046 0.04 0.00 0.03 0.00 0.02 0.00 < 0.03 — ± ± ± ac114 048 0.08 0.01 0.06 0.00 0.04 0.01 < 0.03 — ± ± ± ac114 049 0.04 0.00 0.03 0.00 0.02 0.00 < 0.03 < 0.02 ± ± ± ac114 050 0.07 0.01 0.06 0.00 0.04 0.01 < 0.03 — ± ± ± ac114 051 0.04 0.00 0.03 0.00 0.02 0.00 < 0.03 — ± ± ± ac114 052 0.16 0.01 0.13 0.01 0.08 0.01 0.08 0.02 — ± ± ± ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν(4.5 m) Fν(5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
ac114 053 0.18 0.01 0.15 0.01 0.10 0.01 0.07 0.01 < 0.07 ± ± ± ± ac114 054 0.02 0.00 0.02 0.00 < 0.01 < 0.03 — ± ± ac114 055 0.04 0.00 0.03 0.00 0.02 0.00 < 0.03 < 0.07 ± ± ± ac114 056 0.04 0.00 0.03 0.00 0.02 0.00 < 0.03 — ± ± ± ac114 058 0.04 0.00 0.04 0.00 0.02 0.00 < 0.03 — ± ± ± ac114 059 0.03 0.00 0.03 0.00 0.02 0.00 < 0.03 — ± ± ± ac114 060 0.01 0.00 0.01 0.00 < 0.02 < 0.03 — ± ± 79 ac114 061 0.01 0.00 0.01 0.00 < 0.02 < 0.02 < 0.09 ± ± ac114 062 0.07 0.01 0.06 0.00 0.03 0.01 < 0.03 < 0.08 ± ± ± ac114 063 0.05 0.00 0.04 0.00 0.02 0.01 < 0.03 < 0.07 ± ± ± ac114 064 0.04 0.00 0.04 0.00 0.02 0.00 < 0.03 < 0.02 ± ± ± ac114 065 0.03 0.00 0.03 0.00 0.02 0.00 < 0.03 < 0.07 ± ± ± ac114 066 0.62 0.04 0.51 0.04 0.32 0.02 0.22 0.02 — ± ± ± ± ac114 067 0.04 0.00 0.03 0.00 0.03 0.00 < 0.03 < 0.07 ± ± ± ac114 068 0.13 0.01 0.10 0.01 0.07 0.01 0.05 0.01 — ± ± ± ± ac114 069 0.09 0.01 0.08 0.01 0.06 0.01 < 0.06 < 0.20 ± ± ± ac114 071 0.11 0.01 0.08 0.01 0.06 0.01 0.04 0.01 < 0.10 ± ± ± ± ac114 072 — — — — < 0.14 ac114 073 0.03 0.00 0.02 0.00 0.02 0.00 < 0.03 < 0.10 ± ± ± (continued) Table 2.4—Continued
Name Fν (3.6 m) Fν(4.5 m) Fν (5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
ac114 074 0.14 0.01 0.12 0.01 0.07 0.01 0.05 0.01 — ± ± ± ± ac114 075 0.11 0.01 0.09 0.01 0.05 0.01 0.04 0.01 — ± ± ± ± ac114 076 0.02 0.00 0.02 0.00 < 0.01 < 0.03 — ± ± ac114 077 0.08 0.01 0.07 0.01 0.04 0.01 < 0.05 < 0.05 ± ± ± ac114 078 0.04 0.00 0.03 0.00 0.02 0.00 < 0.03 — ± ± ± ac114 081 0.36 0.03 0.29 0.02 0.18 0.02 0.12 0.02 — ± ± ± ± ac114 082 0.04 0.00 0.03 0.00 0.02 0.00 < 0.03 < 0.07 ± ± ± 80 ac114 083 0.17 0.01 0.14 0.01 0.10 0.01 0.09 0.01 < 0.10 ± ± ± ± ac114 084 0.02 0.00 0.01 0.00 < 0.01 < 0.03 — ± ± ac114 086 0.04 0.00 0.04 0.00 0.03 0.00 0.10 0.01 0.45 0.11 ± ± ± ± ± ac114 089 0.14 0.01 0.11 0.01 0.07 0.01 0.04 0.01 — ± ± ± ± ac114 090 0.11 0.01 0.09 0.01 0.07 0.01 0.06 0.01 < 0.09 ± ± ± ± ac114 091 0.06 0.00 0.05 0.00 0.03 0.01 < 0.03 < 0.07 ± ± ± ac114 092 0.01 0.00 0.01 0.00 < 0.02 < 0.04 — ± ± ac114 093 0.17 0.01 0.14 0.01 0.09 0.02 0.07 0.02 < 0.18 ± ± ± ± ac114 094 0.09 0.01 0.07 0.01 0.04 0.01 < 0.03 — ± ± ± ac114 095 0.28 0.02 0.23 0.02 0.14 0.01 0.10 0.02 < 0.07 ± ± ± ± ac114 100 — 0.04 0.00 — 0.08 0.02 0.26 0.07 ± ± ± ac114 102 0.01 0.00 0.01 0.00 < 0.01 < 0.03 < 0.09 ± ± (continued) Table 2.4—Continued
Name Fν (3.6 m) Fν(4.5 m) Fν (5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
ac114 103 0.02 0.00 0.01 0.00 < 0.01 < 0.03 < 0.07 ± ± ac114 105 0.06 0.01 0.05 0.00 < 0.03 < 0.06 — ± ± ac114 106 0.12 0.01 0.13 0.01 0.14 0.01 0.10 0.01 0.50 0.12 ± ± ± ± ± ac114 107 0.26 0.02 0.22 0.02 0.13 0.01 0.09 0.02 < 0.07 ± ± ± ± ac114 110 0.03 0.00 0.02 0.00 0.02 0.00 < 0.03 — ± ± ± ac114 111 0.01 0.00 0.01 0.00 < 0.02 < 0.04 < 0.10 ± ± ac114 112 0.04 0.00 0.04 0.00 0.03 0.00 0.06 0.01 0.23 0.06 ± ± ± ± ± 81 ac114 114 0.04 0.00 0.03 0.00 0.02 0.01 < 0.03 < 0.07 ± ± ± ac114 115 — 0.74 0.05 — 0.34 0.05 < 0.12 ± ± ac114 116 0.12 0.01 0.13 0.01 0.10 0.01 0.50 0.04 1.57 0.37 ± ± ± ± ± ac114 118 0.06 0.00 0.05 0.00 0.04 0.01 0.05 0.01 0.12 0.04 ± ± ± ± ± ac114 119 0.01 0.00 0.01 0.00 < 0.01 < 0.03 — ± ± ac114 120 0.15 0.01 0.14 0.01 0.08 0.01 0.14 0.02 0.36 0.10 ± ± ± ± ± ac114 121 0.08 0.01 0.06 0.00 0.04 0.01 < 0.04 < 0.03 ± ± ± ac114 122 0.00 0.00 0.00 0.00 — < 0.03 < 0.03 ± ± ac114 123 0.11 0.01 0.10 0.01 0.08 0.01 0.18 0.03 0.41 0.11 ± ± ± ± ± ac114 124 0.05 0.00 0.05 0.00 0.03 0.01 0.09 0.01 0.51 0.13 ± ± ± ± ± ac114 126 — 0.21 0.02 0.12 0.02 0.08 0.02 < 0.14 ± ± ± ac114 127 — 0.36 0.03 0.24 0.03 0.24 0.03 0.30 0.09 ± ± ± ± (continued) Table 2.4—Continued
Name Fν (3.6 m) Fν(4.5 m) Fν (5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
ac114 129 0.05 0.00 0.04 0.00 0.03 0.01 < 0.03 < 0.03 ± ± ± ac114 130 0.02 0.00 0.01 0.00 < 0.01 < 0.03 — ± ± ac114 131 0.01 0.00 0.01 0.00 < 0.02 < 0.05 < 0.06 ± ± ac114 132 0.10 0.01 0.08 0.01 0.05 0.01 < 0.03 — ± ± ± ac114 135 — 0.08 0.01 — < 0.06 < 0.12 ± ac114 137 0.03 0.00 0.02 0.00 0.01 0.00 < 0.03 < 0.09 ± ± ± ac114 138 0.05 0.00 0.06 0.00 0.04 0.01 0.20 0.02 0.67 0.15 ± ± ± ± ± 82 ac114 139 0.11 0.01 0.11 0.01 0.08 0.01 0.28 0.02 0.80 0.19 ± ± ± ± ± ac114 140 0.23 0.02 0.17 0.01 0.11 0.01 < 0.06 < 0.09 ± ± ± ac114 141 0.02 0.00 0.02 0.00 < 0.03 < 0.06 < 0.15 ± ± ac114 142 0.02 0.00 0.02 0.00 < 0.02 < 0.04 0.11 0.04 ± ± ± ac114 143 0.01 0.00 0.01 0.00 < 0.02 < 0.05 < 0.11 ± ± ac114 146 0.02 0.00 0.02 0.00 < 0.02 < 0.05 — ± ± ac114 147 0.01 0.00 0.01 0.00 < 0.02 < 0.05 — ± ± ac114 149 0.22 0.02 0.17 0.01 0.14 0.01 0.72 0.05 1.21 0.28 ± ± ± ± ± ac114 150 0.14 0.01 0.12 0.01 0.08 0.01 0.11 0.02 — ± ± ± ± ac114 152 0.06 0.00 0.05 0.00 0.03 0.01 < 0.06 — ± ± ± ac114 153 0.01 0.00 0.01 0.00 < 0.02 < 0.04 < 0.09 ± ± ac114 154 0.02 0.00 0.02 0.00 < 0.02 < 0.04 < 0.12 ± ± (continued) Table 2.4—Continued
Name Fν (3.6 m) Fν(4.5 m) Fν (5.8 m) Fν(8.0 m) Fν(24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
ac114 155 0.06 0.01 0.07 0.01 0.06 0.01 0.25 0.03 0.55 0.13 ± ± ± ± ± ac114 156 0.06 0.00 0.05 0.00 0.04 0.01 0.14 0.02 0.39 0.10 ± ± ± ± ± ac114 157 0.01 0.00 < 0.01 < 0.04 < 0.06 — ± ac114 160 0.01 0.00 0.01 0.00 < 0.02 < 0.04 < 0.09 ± ± ac114 161 0.15 0.01 — — 0.08 0.02 < 0.11 ± ± ac114 162 0.02 0.00 0.02 0.00 < 0.02 < 0.05 — ± ± ac114 163 0.11 0.01 0.09 0.01 0.07 0.01 0.15 0.02 0.47 0.12 ± ± ± ± ± 83 ac114 164 0.03 0.00 0.03 0.00 0.02 0.01 0.04 0.01 — ± ± ± ± ac114 165 0.02 0.00 0.01 0.00 < 0.02 < 0.06 < 0.09 ± ± ac114 166 0.01 0.00 < 0.02 — — — ± ac114 167 — 0.12 0.01 — 0.35 0.08 2.13 0.48 ± ± ± ac114 169 0.22 0.02 0.17 0.01 0.12 0.01 < 0.06 — ± ± ± ac114 170 0.07 0.01 0.08 0.01 0.06 0.01 0.26 0.03 0.47 0.12 ± ± ± ± ± ac114 174 0.03 0.00 0.03 0.00 < 0.02 0.05 0.02 < 1.58 ± ± ± ac114 176 0.08 0.01 0.06 0.01 0.05 0.01 0.07 0.02 < 0.14 ± ± ± ± ac114 177 0.03 0.00 0.03 0.00 < 0.02 — — ± ± ac114 178 0.23 0.02 0.24 0.02 0.19 0.02 0.74 0.07 2.40 0.55 ± ± ± ± ± ac114 181 0.04 0.00 0.03 0.00 < 0.03 < 0.06 < 0.09 ± ± ac114 182 — 0.08 0.01 — < 0.08 — ± (continued) Table 2.4—Continued
Name Fν(3.6 m) Fν (4.5 m) Fν(5.8 m) Fν(8.0 m) Fν (24 m) (mJy) (mJy) (mJy) (mJy) (mJy) (1) (2) (3) (4) (5) (6)
ac114 185 0.01 0.00 — < 0.02 < 0.04 < 0.09 ± ac114 188 0.09 0.01 0.10 0.01 0.07 0.01 0.28 0.03 0.78 0.20 ± ± ± ± ± ac114 190 0.03 0.00 0.03 0.00 0.02 0.01 < 0.04 — ± ± ± ac114 191 — — — < 0.11 < 0.15 ac114 192 0.04 0.00 0.04 0.00 0.03 0.01 0.13 0.02 0.35 0.09 ± ± ± ± ± ac114 193 0.01 0.00 0.01 0.00 < 0.02 < 0.04 —
84 ± ± ac114 197 0.13 0.01 0.13 0.01 0.09 0.01 0.36 0.03 0.96 0.25 ± ± ± ± ± ac114 199 0.03 0.00 0.03 0.00 0.02 0.01 0.07 0.02 < 0.15 ± ± ± ± ac114 202 0.01 0.00 0.01 0.00 < 0.02 < 0.04 < 0.12 ± ±
Note. — MIR photometry for the identified cluster members. (1) The name of this object, constructed from a shorthand of its parent cluster and the order in which each object appears in the list of cluster members extracted from NED. (2 6) MIR fluxes measured in R band Kron like aperture. Where appropriate, 3σ upper limits on measured MIR fluxes, derived from the appropriate uncertainty mosaic, are given. Galaxies with no quoted upper limit for a given band have no coverage in the corresponding image. Chapter 3
Physical Member Properties and Statistical Methods
I want to examine AGN and SFGs in the cluster sample introduced in 2.1. § However, this requires that I identify a consistent method to select AGN from among the X ray sources identified by M06 and to measure stellar masses and SFRs. Furthermore, these measurements must be reliable not only in normal cluster galaxies, but also in the presence of an AGN. In some cases, I also require corrections from the observed sample of cluster members to the complete underlying population. The first step in all of these tasks is to fit model SEDs to the measured fluxes after they have been corrected as described in 2.3.1. I describe the model SEDs in 3.1. § § With these model SEDs, I identify AGN 3.1.1, calculate stellar masses 3.1.3 and § § SFRs 3.1.4. §
Given the measured galaxy properties, I also want to examine the statistical behavior of galaxies and AGN in the cluster sample. I employ partial correlation analysis to identify the strongest correlations between star formation and different galaxy observables. I introduce the formalism of partial correlation analysis in 3.2. § I also want to examine the average dependence of galaxy properties on their local environment within the cluster, but this requires that I correct from the observed sample of cluster members to the complete, underlying population. To do this, I develop completeness corrections ( 3.3) that I will employ extensively in Chapter 5. § 85 3.1. Model SEDs
Assef et al. (2010; hereafter A10) constructed empirical SED templates that can be used to determine photometric redshifts and K corrections for galaxies and AGN over a wide range of redshifts. The A10 templates include three galaxy templates (elliptical, spiral, and starburst or irregular) and a single AGN template, which can be subjected to variable intrinsic reddening. These templates were derived empirically across a long wavelength baseline (0.03–30 m), using 14448 apparently “pure” galaxies and 5347 objects with AGN signatures. I fit two independent model SEDs to the photometry of each cluster member using the published codes of A10. The first model includes only the three galaxy templates, while the second also includes an AGN component. The χ2 differences between the two fits can be used to identify AGN (Section 3.1.1). Model SEDs for the M06 X ray point sources included in the sample of “good” galaxies are shown in Figure 3.1. AGN identified from their SED fits, but which are not identified from their X ray luminosities, are shown in Figure 3.4. The fits to the X ray point sources are representative of the fit quality returned for all cluster members, while the fits to photometrically identified AGN are slightly worse than average.
The model SEDs fit to 25 of the 488 spectroscopically identified cluster members are poorly matched to the measured photometry (χ2 > 25). I determine photometric redshifts for all of the identified cluster members, and in cases where the measured photometric redshifts are more than 3σ away from the cluster redshift, I replace the spectroscopic redshifts with photometric redshifts and repeat the fit. In 11 cases, this procedure results in substantial improvements to the fits ( χ2 > 12,
2 χphoto−z < 4). This suggests that some galaxies in the sample have erroneous spectroscopic redshifts. One such object is an X ray source, identified as AC 114 5
86 by M06. The redshift for this object was reported by Couch et al. (2001; their galaxy #365). The spectra used by these authors covered a relatively narrow wavelength range (8350A˚ <λ< 8750A)˚ and had moderately poor S/N. I suspect that these factors, in concert with a strong prior in favor of cluster membership in the presence of a putative Hα emission line at the correct redshift, led Couch et al. (2001) to mis identify the [Oii]λλ4354 and [Oiii]λλ4363 emission lines of a background AGN at z = 0.988 as the [Nii]λλ6548 and Hα emission lines, respectively, at the cluster redshift. Four of the 5 objects flagged as having erroneous redshifts in AC 114 have redshifts from Couch et al. (2001). Two of the four have redshifts from only one emission line, and both objects with redshifts from multiple emission lines have plausible pairs of lines that might be mistaken as other line pairs at the redshift of the cluster. Furthermore, all of the objects with apparently erroneous redshifts are quite faint, having V < 22, which makes acquiring high S/N spectra difficult. The ∼ identification of objects with discrepant photometric and spectroscopic redshifts as interlopers appears to be reliable, and I eliminate the associated galaxies from further consideration. The absence of AC 114 5 from the X ray AGN sample has important repercussions for the conclusions drawn by M07 from this AGN sample, which I discuss in 4.5. §
3.1.1. AGN Identification
Different AGN selection techniques identify different AGN populations and suffer from distinct selection biases. Radio selection finds AGN primarily in very massive galaxies and in the densest environments (Hickox et al. 2009). Both X ray and visible wavelength techniques can miss AGN due to absorption, either in the host galaxy or in the AGN itself; however, X ray selection can find lower luminosity
87 AGN and AGN behind larger absorbing columns compared to emission line selection. Mid infrared selection techniques suffer from relatively poor angular resolution, so they are mainly sensitive to AGN that outshine their host galaxies in the band(s) used to perform the AGN selection. The X ray and visible techniques can also be contaminated by emission from the host galaxy. While the identification of X ray
42 −1 sources with LX > 10 erg s as AGN is unambiguous, X ray luminosities in the 1040–1042 erg s−1 range can be produced by low mass X ray binaries (LMXBs), high mass X ray binaries (HMXBs), and thermal emission from hot gas. Both visible wavelength and MIR indicators are subject to contamination from hot stars, which produce emission lines and heat dust near star forming regions until it emits in the MIR. Even the interpretation of the well established Baldwin Phillips Terlevich diagram (Baldwin et al. 1981) can be controversial in the transition region between star forming galaxies and AGN (Cid Fernandes et al. 2010). These difficulties motivate the use of multiple techniques to obtain a complete census of AGN and to correctly identify potential imposters.
X-ray Selection
I consider AGN selected based on their X ray luminosities, the shapes of their
42 −1 SEDs, or both. X ray sources with LX > 10 erg s are unambiguously AGN, but several non AGN processes can produce X ray luminosities in the 1040– 1042 erg s−1 range. These include LMXBs, HMXBs and a galaxy’s extended, diffuse halo gas. The integrated X ray luminosities of LMXBs and hot halo both correlate strongly with stellar mass, as measured by the galaxy’s K band luminosity (Kim & Fabbiano 2004; Sun et al. 2007), and the luminosity from HMXBs correlates with SFR (Grimm et al. 2003). These correlations allow me to predict the X ray luminosity of a normal
88 galaxy using only parameters that can be measured from the model SEDs. Similar analyses were performed by Sivakoff et al. (2008) and Arnold et al. (2009), who employed K band luminosities measured directly from 2MASS photometry.
I infer K band magnitudes from the model SEDs and determine SFRs from the K corrected 8 m and 24 m luminosities of X ray sources in each cluster. I then use
LK and SFR in Eqns. 3.1, 3.2 and 3.3 to predict the expected X ray luminosities from the host galaxies of X ray point sources identified by M06 (Kim & Fabbiano 2004; Grimm et al. 2003; Sun et al. 2007, respectively). The predictions for X ray emission from a given galaxy due to LMXBs, HMXBs and the thermal halo are accurate to within 0.3 dex and are given by, ∼
30 −1 LK LX(LMXB;0.3 8 keV) = [(0.20 0.08) 10 erg s ] (3.1) − ± × LK,⊙
1.7 39 −1 SF R LX(HMXB)=2.6 10 erg s −1 (3.2) × M⊙ yr
1.63±0.13 39 −1 LKs LX(thermal;0.5 2 keV) =2.5 10 erg s 11 (3.3) − × 10 L⊙ where LK and LKs are the galaxy’s luminosities in the K and Ks filters. Each relation is given in slightly different energy ranges, none of which coincide exactly with the range used by M06. This problem is especially severe for Eqn. 3.2, because Grimm et al. (2003) take their X ray fluxes from various sources in the literature without converting them to a common energy range. They claim that the resulting uncertainty is small because the scatter in the relation is much larger than the bandpass corrections. Fortunately, even if this were not the case, the HMXB contribution to the total predicted X ray luminosities is small for the SFRs typical
−1 of cluster galaxies (< 10 M⊙ yr ). In order to predict host X ray luminosities ∼ in the same bands for all production channels, I convert Eqns. 3.1 3.3 to predict
89 luminosities in the soft X ray (0.5 2 keV) and hard X ray (2 8 keV) bands. To accomplish this, I assume a Γ = 1.7 power law for the LMXB and HMXB relations, which is typical for these types of sources as well as for AGN. I further assume that the Grimm et al. (2003) relation corresponds to luminosities in the 2 10 keV range and that the thermal emission from the kT = 0.7 keV halo gas is negligible in the hard X ray band.
Sun et al. (2007) fit the same X ray bands I employ to the measured LK of the galaxies they examine, so there is no need to transform their fits. This is important, since the thermal emission from hot gas is the dominant component of the soft X ray
40 −1 emission for Lsoft > 6 10 erg s ; however, this transition luminosity depends ∼ × on the specific form adopted in Eqn. 3.3. Mulchaey & Jeltema (2010) found that L (corona) L3.9±0.4 for field galaxies, which differs significantly from the results X ∝ K of Sun et al. (2007). While the Mulchaey & Jeltema (2010) relation is not strictly applicable to the cluster galaxies considered here, the difference between cluster and field galaxies suggests that the thermal X ray emission from a galaxy’s halo depends on its environment. Such a variation introduces a systematic uncertainty in L (corona) of up to 0.8 dex at L =4 1011L . I neglect this uncertainty in the X K × ⊙ X ray AGN selection, as the correction appropriate for a given cluster is impossible to quantify given the data presently available.
Selection From SED Shape (IR AGN)
An alternative method to identify AGN is to use the distinctive shape of their SEDs, particularly in the MIR (e.g. Marconi et al. 2004; Stern et al. 2005; Richards et al. 2006; A10). This approach can identify AGN behind gas column densities large enough to obscure even the X rays emitted by an AGN. Such an AGN sample
90 has very different selection criteria and biases than an X ray selected sample, and combining the two results in more complete AGN identification.
I identify AGN from their SEDs by comparing the goodness of fit of two sets of model templates. The first set uses only the normal galaxy templates. The second also includes the AGN template. I determine whether a given galaxy requires an AGN component in its model SED by applying a threshold on the likelihood ratio, ρ,
exp[ χ2(gal)/2] ρ = − (3.4) exp[ χ2(gal + AGN)/2] − where χ2(gal) and χ2(gal + AGN) are goodnesses of fit for a model with only the A10 galaxy templates and for a model that includes an additional AGN component, respectively. AGN are those objects whose ρ is smaller than a pre determined selection limit, ρmax, established by Monte Carlo simulations of normal galaxies.
To determine an appropriate ρmax, I combine the three galaxy templates of A10 in proportions that reflect the template luminosity distributions in real cluster members. I introduce Gaussian photometric errors comparable to the photometric uncertainties in the real data (0.07 mag) to the fluxes given by the model SEDs and allowed occasional catastrophic errors of up to 0.3 dex. The artificial galaxy photometry does not include upper limits, which I also neglect when constructing model SEDs of real galaxies. I fit the artificial galaxies with two models. The first model excludes the AGN component from the fit, while the second component includes it. The likelihood ratio distributions computed from the goodness of fit results for the two different models are shown in Figure 3.6. These distributions show the probability that a pure galaxy will be erroneously classified as an AGN due to the presence of photometric errors. The similarity of the different distributions, even based on only 4 photometric bands, indicates that a single ρmax can be used to select AGN from among all galaxies in the sample. I fix ρ = 1.5 10−3, which is the max × 91 99.8 percentile point of the ρ distribution returned by fits to model SEDs for normal galaxies ( 3.1.1). On average, there should be 1 false position AGN identification § in a sample of AGN selected from among the entire set of good cluster members. The IR AGN sample should have 3 or fewer false positives at 98% confidence, which implies 90% purity. ≥
Alternatively, I identify AGN based on the F statistics of the two model SED fits described above. The advantage of this approach is that it identifies objects with unusual SEDs compared to the sample of cluster members instead of artificial SEDs. Figure 3.7 shows the F statistic as a function of χ2(gal) for X ray AGN selected using Figure 4.1, AGN selected using likelihood ratios, and “normal” cluster members. The F statistic is given by, χ2/2 F = 2 (3.5) χν(gal + AGN) where χ2 is the (absolute) change in the total χ2 after introducing the AGN component to the fit. In addition to the galaxies that are well fit by the galaxy only model and not substantially improved by the addition of an AGN component, there are objects with large χ2(gal) but small F , and objects with large F but small χ2(gal). Neither of the latter two categories contain objects likely to be AGN from the point of view of the model SEDs. The most luminous X ray AGN have both large F and large χ2(gal). These are clearly identified as AGN by the model SEDs, and less luminous X ray AGN can be found with increasing density toward the normal galaxy locus at the origin of Figure 3.7. The dotted and dashed lines in the Figure correspond to the ρ < ρmax selection boundaries for N=6 and N=9 flux measurements, respectively. Some objects above the N=9 line are not selected as IR AGN because they fail a cut on the overall goodness of fit, which requires
2 χν(gal + AGN) < 5. While it would be possible to define an AGN selection region in Figure 3.7, the non uniformity of the photometry would lead to different effective
92 cuts in χ2 between different clusters and between objects in individual clusters. Furthermore, only 3 AGN identified using likelihood ratios fall into the suspect part of Figure 4 with F 1. This level of contamination ( 10%) is consistent with the ≈ ∼ estimated purity of the X ray AGN, which I deem to be acceptable, and it agrees with the expectations from Figure 3.6. Therefore, for the rest of this work, I rely on a likelihood ratio threshold to identify AGN. Likelihood ratio selection of AGN from SED fits is most sensitive to the shape of the MIR SED, so I will henceforth refer to AGN so identified as IR AGN.
I estimate the completeness of the IR AGN sample as a function of the reddening of the AGN template and luminosity using Monte Carlo simulations. I construct model AGN SEDs by injecting an AGN component with some luminosity and reddening into artificial galaxy photometry, which I generate using the techniques described above to construct a sample of artificial galaxy SEDs. The completeness of the IR AGN sample follows from the fraction of such AGN recovered by the SED selection. Predictably, the completeness depends strongly on the luminosity of the AGN component. The algorithm cannot consistently identify AGN with
10 Lbol < 7 10 L⊙. However, the completeness depends only weakly on E(B V ). ∼ × − There are measurable differences only for AGN with E(B V ) > 2. For the observed − wavelengths, AGN identification depends most strongly on the shape of the MIR SED, which is insensitive to modest amounts of reddening. The full dependence of completeness on L and E(B V ) is listed in Table 3.2. bol −
I caution that both the AGN identification and correction use the fixed AGN template derived by A10. While this template is dominated by luminous AGN, AGN of all luminosities were used in its construction, and in some sense it represents the optimal median AGN SED. There is some evidence that AGN with low Eddington ratios (Lbol/LEdd) are systematically weaker in the UV and the MIR than higher
93 L /L AGN. This appears to become important at L /L 10−3 (Ho 2008). bol Edd bol Edd ≈ However, the UV weakness of such objects remains a subject of debate (e.g. Ho 1999, 2008; Dudik et al. 2009; Eracleous et al. 2010), and the SEDs of AGN appear to all be quite similar out to λ 20 m, even in AGN with accretion rates as low as ≈ L /L 10−3 (Ho 2008, Figure 7). Furthermore, the variable reddening of the bol Edd ≈ AGN component allowed by the models can account for differing UV/visible flux ratios, making the AGN component of the model SEDs flexible enough to mimic AGN with a wide variety of Eddington ratios.
Intrinsic variations in the AGN SED might account for the absence of an important AGN component in the SEDs of many X ray AGN, despite their similar distributions in L ( 4.4). Another possible explanation is that the MIR emission bol § from many X ray AGN could be overwhelmed by star formation in their host
42 −1 galaxies. I find that X ray AGN with LX > 10 erg s that are also identified as IR AGN have no measurable star formation, while those not identified in the IR have SF R = 0.3 M yr−1. This may be a selection effect, since only AGN identified ⊙ with the SED technique are corrected for the presence of MIR emission from the AGN. However, it appears that the balance between SFR and nuclear emission is an important factor in determining whether a given X ray source will be identified as an IR AGN.
Also of concern is the MIR emission exhibited by some normal galaxies which is clearly not excited by young stars (e.g. Verley et al. 2009; Kelson & Holden 2010). The strength of the diffuse interstellar dust emission relative to star formation varies from galaxy to galaxy depending on the populations of AGB stars, which can produce and heat dust (Kelson & Holden 2010), and field B stars (including horizontal branch stars), which produce UV light that can both heat dust grains and excite PAH emission in the diffuse ISM (e.g. Li & Draine 2002). These effects could
94 mimic the presence of an AGN, particularly in passively evolving galaxies, which the A10 templates predict should decline strictly as a νF ν2.5 power law. Given ν ∝ the limited data available to constrain MIR emission not associated with either an AGN or a star forming region and the as yet uncertain magnitude of the associated variations, I neglect any potential effects on the IR AGN identification. However, sources of MIR emission not accounted for by the A10 templates, especially emission from dust heated by old stars in passive galaxies, remain a potentially important systematic uncertainty.
3.1.2. Bolometric AGN Luminosities
In order to conduct a meaningful comparison of X ray and IR AGN, I must place them on a common luminosity system. The most obvious choice is the bolometric AGN luminosity (Lbol), which also allows me to examine black hole growth rates.
The A10 AGN template provides a natural means of determining the bolometric luminosity (Lbol) for IR AGN, but the MIR luminosity in the template comes from reprocessed dust emission, which would result in double counting the UV emission from the disk for AGN viewed face on (Marconi et al. 2004, hereafter M04; Richards et al. 2006). Instead, I determine Lbol using a piecewise combination of the AGN model SED and three power laws. I integrate the un reddened A10 AGN template from Lyα to 1 m, shortward of which the template becomes uncertain due to absorption by the Lyα forest, and I estimate the X ray luminosity by integrating a Γ=1.7 power law from 1–10 keV. The extreme ultraviolet (EUV) is determined by integrating L ν−αox from λ = 1216A˚ to 1 keV. The slope of the EUV SED (α ) ν ∝ ox
95 is given by Eqn. 2 of Vignali et al. (2003),
L (2500A)˚ α =0.1 log ν 1.32 (3.6) ox erg s−1 − with Lν (2500A)˚ taken from the AGN template SED. Finally, I eliminate reprocessed emission from dust by assuming F ν−2 for 1 m<λ< 30 m, following M04. ν ∝
To correct the X ray luminosities of X ray AGN to bolometric luminosities, I fit a power law to the measured L (0.3 8 keV) and L of the 8 IR AGN identified X − bol separately in X rays. A least squares fit to the total X ray and AGN luminosities yields,
L log[L (0.3 8 keV)] = (0.9 0.2) log bol + (41.4 0.2) (3.7) X − ± 1043erg s−1 ± where Lbol is the bolometric AGN luminosity integrated from 10 keV to 30 m. The AGN used to determine Eqn. 3.7 show a scatter of 0.4 dex about the best fit relation (Figure 3.9). Figure 3.9 suggests that the slope returned by the fit may be strongly influenced by the highest luminosity AGN. However, a fit to the other 7 AGN returns an identical slope (0.9 0.5), so Eqn. 3.7 is not significantly biased by the ± highest luminosity object. The luminosity dependence of the bolometric corrections (BCs) derived from the fit is therefore robust. The slope is also consistent, within large statistical uncertainties, with the luminosity dependence derived by M04.
The ad hoc BCs derived from Eqn. 3.7 are fairly crude. For example, the fit does not account for uncertainties on LX or Lbol, which include large systematic components. It also ignores upper limits, which will lead it to over predict the true
LX at fixed Lbol. M04, by contrast, provide luminosity dependent BCs in several energy ranges that account for X ray non detections (their Eqn. 21). I convert their BCs to 0.3–8 keV assuming Γ = 1.7 and estimate the expected X ray flux from the IR AGN. The predicted X ray fluxes exceed those estimated using Eqn. 3.7, which
96 already over estimates the intrinsic LX–Lbol relation, by 0.7 dex or more. This might result if the M04 SED is a poor match to the A10 AGN template. M04 determine their X ray BCs using the αox relation derived by Vignali et al. (2003) for a sample of SDSS quasars, including broad absorption line quasars (BALQSOs). Given that the
Lbol calculation is insensitive to the absorption in BALQSOs, it is possible that the
M04 BCs over estimate LX at fixed Lν (2500A)˚ when applied to the present sample. In order to produce consistent results for the X ray and IR AGN, I therefore use the BCs implied by Eqn. 3.7 rather than the M04 BCs, despite the large statistical uncertainties associated with Eqn. 3.7.
3.1.3. Stellar Masses
Stellar population synthesis modeling provides a means to estimate stellar masses in the absence of detailed spectra. Bell & de Jong (2001) constructed model spectra of galaxies for a wide variety of stellar masses, SFRs, metallicities and stellar initial mass functions (IMFs) to convert colors to mass to light ratios (M/L). Their models assumed a mass dependent formation epoch with bursty star formation histories, which is appropriate for the spiral galaxies they study. Figure 9 of Bell & de Jong (2001) makes it clear, however, that their results also robustly estimate M/L for passively evolving galaxies. In fact, the scatter about the mean M/L tends to decrease for redder systems because the stochasticity of the star formation history becomes less important in galaxies that experienced their last burst of star formation in the distant past.
Bell & de Jong provide a table of coefficients (aλ,bλ) relating M/L for a galaxy to its color,
log (M/L )= a + b color (3.8) 10 λ λ λ × 97 where color is measured in the bands for which aλ and bλ were determined. I adopt the coefficients appropriate for Solar metallicity computed with the Bruzual & Charlot (2003) population synthesis code and the scaled Salpeter IMF suggested by Bell & de Jong (2001). They report that this modified Salpeter IMF, which has total
′ mass M = 0.7MSalpeter, yields the best agreement with the Tully Fisher relation.
Given an appropriate (aλ,bλ) pair, it is straightforward to compute stellar masses from the visible magnitudes. However, these magnitudes must first be corrected for the AGN emission in sources identified as IR AGN.
The uncertainty introduced by the AGN subtraction is a combination of the statistical uncertainty in the contribution of the AGN template to the model SED, which is a property of the fit, and the uncertainty in the AGN template itself. To measure the uncertainty in the template, I examined 1644 luminous quasars with spectroscopic redshifts from the AGN and Galaxy Evolution Survey (AGES; Kochanek et al. in preparation) and determined the variation in their measured photometry about their best fit model SEDs. Using these measurements, I constructed an RMS SED for AGN and averaged it across each of the photometric bands in Tables 2.3 and 2.4. The uncertainty in the AGN correction resulting from intrinsic variation about the AGN template is 10% except at 24 m, where there ∼ are too few z = 0 quasars to make a meaningful comparison. The uncertainty in the AGN correction at 24 m is therefore large, but it can be constrained by the agreement of the 8 m and 24 m SFRs (Figure 3.8) to within a scatter of 0.3 dex.
In galaxies with no genuine nuclear activity the AGN template can contribute to the model to correct for variations in stellar populations relative to the templates, intrinsic extinction, or errors in the measured photometry. Subtraction of the AGN component under these circumstances would result in under estimated stellar masses and SFRs, while failure to subtract the AGN component in a genuine, low luminosity
98 AGN would cause the measured SFRs of their host galaxies to be biased toward higher values. However, the ambiguity between a genuine, low luminosity AGN and an apparent AGN component introduced to correct for photometric errors (Section 3.1.1) renders any attempt to subtract the AGN component in such cases suspect. Therefore, in normal galaxies and in X ray AGN not identified as IR AGN, no AGN correction is applied. I accept the inherent bias to avoid introducing ambiguous AGN corrections, which would be much more difficult to interpret.
The Bell & de Jong (2001) calibrations are reported for rest frame colors, so I require K corrections for each cluster member to convert the measured magnitudes to the rest frame. I calculate the K corrections from the model SEDs returned by the A10 fitting routines. Uncertainties on K corrections cannot be directly determined from the uncertainties in the model components because K corrections depend non linearly on these uncertainties. Therefore, I recombine the components of each model SED in proportion to the uncertainties in their contributions to the total model flux. This results in a series of temporary model SEDs. I then calculate the K corrections implied by these temporary model SEDs and measure their dispersions to estimate the uncertainties in the K corrections returned by the original model SED.
The systematic uncertainty on the stellar masses calculated from Eqn. 3.8 can be estimated by comparing the fiducial masses with masses derived using different assumptions. The systematic uncertainties in stellar mass, listed in Table 4.1, are given by the difference between the fiducial mass and the mass returned with the assumption of a Salpeter IMF and the Pegase´ population synthesis models. The typical value of this systematic uncertainty is 0.2 dex. Conroy et al. (2009) studied ∼ the ability of different models to reproduce the integrated colors of Milky Way
99 globular clusters, and they found that systematic uncertainties on stellar masses derived from population synthesis codes typically reach or exceed 0.3 dex.
3.1.4. Star-Formation Rates
I measure SFRs from the AGN corrected MIR photometry using the empirical relations of Zhu et al. (2008), which have been determined for both the IRAC 8 m and the MIPS 24 m bands using the same calibration sample. While the contribution of the stellar continuum to the observed 24 m luminosity is negligible, the Rayleigh Jeans tail of the stellar continuum emission can make an important contribution to the integrated flux at 8 m, especially in galaxies with the low SFRs typical in clusters. The method used to subtract this contribution is an important systematic uncertainty in the SFR calculation. Zhu et al. (2008) assume that the contribution of the stellar continuum at 8 m can be described
stellar by Lν (8 m)=0.232Lν(3.5 m), as derived from the models of Helou et al. (2004). Under this assumption, Zhu et al. (2008) derive luminosity–SFR relations appropriate for a Salpeter IMF,
νLdust(8 m) SF R(M yr−1)= ν (3.9) ⊙ 1.58 109L × ⊙
νL (24 m) SF R(M yr−1)= ν (3.10) ⊙ 7.15 108L × ⊙ dust stellar where Lν (8 m) is determined by subtracting Lν (8 m) from the measured 8 m luminosity. Sim˜oes Lopes et al. (in preparation) find that
stellar Lν (8 m)=0.269Lν(3.5 m) provides a better estimate for their sample of nearby, early type galaxies with no dust and conclude that the difference in their result compared to Helou et al. (2004) is due to the mass–metallicity relation.
100 Another important systematic uncertainty in SFRs derived from PAHs is the dependence of the PAH abundance on metallicity (Calzetti et al. 2007), because lower metallicity systems have fewer PAHs and therefore weaker 8 m emission at fixed SFR. This second effect is negligible for the high mass—and therefore metal rich—galaxies I consider. I neglect both metallicity and mass dependent effects for the remainder of the analysis. Instead, I follow Zhu et al. (2008) and
stellar assume that Lν (8 m)=0.232Lν(3.5 m) to derive SFRs from Eqns. 3.9 and 3.10. For galaxies with measurable (> 3σ) SFRs from both IRAC and MIPS, I take a geometric mean of the two; otherwise, I use whichever SFR measurement is available. The resulting SFRs for AGN are summarized in Table 4.1.
Equations 3.9 and 3.10 were derived using the extinction corrected Hα luminosity of the associated galaxies. The MIPS SFR determined from Eqn. 3.10 for a galaxy with νL = 7.15 109L is 0.6 dex larger than the SFR derived ν × ⊙ ≈ from the Calzetti et al. (2007) relation, which was calibrated using the Paα emission line. Calzetti et al. (2007) used the Starburst99 IMF, and after accounting for this difference, the resulting discrepancy is reduced to 0.4 dex. The choice of SFR calibration therefore represents an important systematic uncertainty in the measured SFRs. The total systematic uncertainty in SFR is indicated by the significant scatter (0.2 dex) and the small but marginally significant offset (0.1 dex) between the IRAC and MIPS SFRs in Figure 3.8. Since the offset is smaller than both the scatter about the line of equality and the systematic uncertainty when comparing to the Calzetti et al. (2007) result, I neglect it below. However, there remains a 15% uncertainty ∼ in the measured SFRs associated with the discrepancy between the IRAC and MIPS SFR indicators.
101 3.2. Partial Correlation Analysis
When confronted with a system of mutually correlated observables, it can be difficult to establish which variables drive the correlations. However, the ability to distinguish between fundamental and derivative correlations would allow the most value to be extracted from a catalog of stellar masses, SFRs and environment traces, as described in 3.1.3 3.1.4. Partial correlation analysis attempts to measure the § relationship between two variables with all other parameters held fixed, and it is one method to resolve such degeneracies. It has been applied in the past to develop a fundamental plane of black hole activity (Merloni et al. 2003) and to probe the dependence of SFR on both stellar mass and environment simultaneously (Christlein & Zabludoff 2005). I employ the simplest formulation of partial correlation analysis, which relies only on direct measurements and does not account for upper limits.
Consider the simplest case, which is a system of only three variables, xi. This is called the first order partial correlation problem. The correlation coefficient for x1 and x2 at fixed x3 can be expressed as,
ρ12 ρ13ρ23 r12.3 = − (3.11) (1 ρ2 )(1 ρ2 ) − 13 − 23 where ρij is the standard two variable correlation coefficient (e.g. the Pearson or
Spearman coefficients) between xi and xj (Wall & Jenkins 2003). Higher order problems describe systems with more variables. For a system with N variables, the (N 2)th order partial correlation coefficient r of variables x and x can − ij.1...N\{ij} i j be written,
Ci,j rij.1...N\{ij} = − (3.12) Ci,iCj,j where C =( 1)i+jM (Kendell & Stuart 1977). M is a reduced determinant of i,j − i,j i,j the correlation matrix R, where Ri,j = ρij, and ρij is the two variable correlation
102 coefficient of xi and xj. The determinant Mi,j can be interpreted as the total correlation among the variables of the system in the absence of i and j. It is calculated from R with the ith row and jth column eliminated (Kendell & Stuart 1977).
Given a partial correlation coefficient from Eqn. 3.12, the significance of the associated correlation must be evaluated before the result can be interpreted. This is accomplished with σij.1...N\{ij},
1 rij.1...N\{ij} σij.1...N\{ij} = − (3.13) √m N − where rij.1...N\{ij} is the partial correlation coefficient given by Eqn. 3.12, N is the number of variables in the system, and m is the number of objects in the sample.
The statistical significance of rij.1...N\{ij} is then given by applying a Student’s t distribution to σij.1...N\{ij} (Wall & Jenkins 2003).
Partial correlation analysis can take both parametric and non parametric forms. These are analogous to the more commonly applied two variable correlation analyses. Equation 3.12 can be applied to any of the correlation coefficients in common use. However, Eqn. 3.13 is defined for the parametric Pearson’s correlation coefficient, so it is appropriate only for that estimator or the closely related, non parametric Spearman coefficient. I want a non parametric approach to retain the largest possible generality, so I rely on Spearman correlation coefficients in my analysis.
3.3. Completeness Corrections
Given an observed partial correlations between two variables, it is preferable to measure the shape of the underlying dependence, but this requires a statistically complete sample from which to measure the dependence. In essence, I need a
103 method to correct for selection effects. The spectroscopic selection function that defines the cluster member sample is unknown, because many of the authors who contribute redshifts to the literature do not define their target selection functions or rates of success. Furthermore, the MIR observations do not uniformly cover the cluster fields, so the observations are more sensitive to star formation in some parts of the cluster than others. Therefore, I empirically determine both the spectroscopic and MIR selection functions to correct for these effects.
3.3.1. Spectroscopic Completeness
I examine only galaxies that have spectroscopic redshifts that confirm cluster membership. These redshifts originate from many sources, primarily Martini et al. (2006), but supplemented with redshifts from the literature. This results in a complex selection function that is a priori unknown. However, this completeness function is required to correct the properties of observed cluster galaxies to the intrinsic distribution for all cluster members. I take an empirical approach to determine spectroscopic completeness and correct the measured cluster members to the total cluster galaxy population.
For each cluster, I bin galaxies identified in the photometric source catalog by V R color, R band magnitude and R/R . The fraction of galaxies with − 200 spectra in each bin (fspec) is then simply given by the number of galaxies in that bin with published spectroscopic redshifts. There are significant variations in fspec as a function of R/R200 and mR, but the dependence on color is at most minor. A partial correlation analysis of fspec as a function of color, magnitude and position shows no significant partial correlation with V R at 95% confidence in any cluster, − while fspec correlates with both mR and R/R200 at > 99.9% confidence. I collapse
104 the completeness measurements along the color axis and determine the fraction of galaxies with spectroscopy as a function of R magnitude and position only. This results in better measurements due to the larger samples that go into each bin.
The fspec described above is one way to express the spectroscopic completeness of galaxies in a given magnitude radius bin. However, the real goal is to find an expression for the spectroscopic completeness, Cspec, of cluster members,
NCl,spec( x) Cspec( x) = (3.14) NCl( x) N ( x) N ( x) N ( x) = spec Cl,spec tot (3.15) Ntot( x) × Nspec( x) × NCl( x)
where x is the position of a given bin in magnitude radius space, NCl,spec is the number of galaxies with spectra that are cluster members, NCl is the number of true cluster members, Nspec is the number of galaxies with spectra in the cluster field, and Ntot is the number of galaxies in the input catalog. All of these quantities except
NCl can be measured directly from the input catalogs. Thus, to use Eqn. 3.14, I would need to infer NCl using some additional piece of information, so I prefer to use fspec instead of the more complicated method in Eqn. 3.14 if possible.
If the redshifts reported in the literature were not pre selected for cluster membership or if the redshift failure rate was high, fspec( x) would be a good proxy for Cspec( x), and the approach in Eqn. 3.14 could be avoided. If this were the case, the fraction of galaxies with spectra that are cluster members (fmem) should drop with R/R200 as the fraction of field galaxies increases. Figure 3.10 shows the results of this test.
Figure 3.10 clearly demonstrates that fmem does not always trace the decline in the density of cluster galaxies. This implies that fspec is not a good tracer of Cspec,
105 and the more sophisticated approach of Eqn. 3.14 is required. Before I can employ Eqn. 3.14, I need to know the number of cluster galaxies in each bin. To do this, I estimate the number of field galaxies in the bin with the R band magnitude number density relation reported by K¨ummel & Wagner (2001). To calculate the number of cluster galaxies, I subtract the estimated number of field galaxies from the total number of galaxies in the bin.
This approach introduces two types of uncertainty. The first is simple Poisson counting uncertainties due to the small number of field galaxies, typically a few to 10, in each bin. The second is cosmic variance. Ellis (1987) reports a B band magnitude number relation that includes measurements from a number of other authors. The different surveys use fields of different sizes, so the scatter of their results about the best fit relation provides a measure of the cosmic variance. Cosmic variance contributes of order 10% uncertainty on the number of field galaxies in a typical bin. The number of field galaxies in a given bin depends on magnitude and cluster mass, but it is typically 1 10 galaxies. At faint magnitudes, the number ∼ of field galaxies is generally comparable to the number of cluster galaxies, and Poisson fluctuations in the number of field galaxies drive the uncertainties in the completeness measurements. The completeness measurements for each cluster are summarized in Table 3.3.
Figure 3.11 shows the spectroscopic completeness to cluster members for 6 of the 8 galaxy clusters in the sample. The remaining 2 clusters (A644 and A2163) have too few members to make a reliable measurement. The dashed, vertical lines on the right column of Figure 3.11 indicate the observed magnitude that corresponds to M = 20 for a typical K correction. The follow up spectroscopy of X ray R − sources conducted by Martini et al. (2006) is only complete for M < 20. Clearly, R − completeness becomes quite poor below this luminosity in all clusters.
106 I could consider completeness as a function of luminosity or stellar mass instead of mR. However, these quantities have higher uncertainties than observed magnitudes, especially for galaxies without spectroscopic redshifts to fix their distances. Therefore, I restrict the sample to galaxies with M 20. R ≤ −
3.3.2. Mid-Infrared Completeness
The depths of the MIR mosaics vary as a function of position across the clusters. This is a result of the Spitzer mosaicking schemes, which provide full coverage of the known X ray point sources in the cluster at the expense of uniformity. These mosaic schemes lead to variations in the number of overlapping images, and therefore to variations in sensitivity, across the cluster fields.
In addition to these sensitivity variations, the Spitzer footprint features some non overlapping coverage by the IRAC bands. This results from the IRAC mapping strategy, which simultaneously images two bands in adjacent fields. The pointings chosen by the observer then determine the degree of overlap between IRAC bands. For a galaxy to enter the final sample, it must include detections in at least 5 bands to ensure that the fit results for that galaxy are well constrained. This means that a galaxy in a region of a cluster with overlapping 3.6 m and 4.5 m images, for example, might be more likely to appear in the final sample than an identical galaxy in a part of the cluster with only 4.5 m coverage.
To construct ensemble statistics for whole clusters, I require sensitivity corrections that account for variable depth across the cluster fields and for the different footprints in the Spitzer bands. Again, these corrections are derived from the MIR data themselves. I measure the MIR flux uncertainties at the locations of all
107 confirmed cluster members from the Spitzer uncertainty mosaics. At each position, I combine the two A10 star forming templates with arbitrary flux normalizations
−2 −1 2 1000 times to produce galaxies with 10 < SFR/1 M⊙ yr < 10 . The flux uncertainties at the positions of cluster members then determine whether the artificial galaxy could be detected at 3σ at the position of each cluster galaxy. I bin the results by 8 m and 24 m fluxes and by R/R200 to estimate completenesses in each band. Figure 3.12 shows the results of this measurement for the 6 clusters in Figure 3.11. IRAC and MIPS completenesses clearly depend on both flux and
R/R200.
The uncertainties in MIR completeness result from the incomplete spectroscopic sampling of the galaxies in a given bin. I assume that the identified cluster members in each bin are representative of the behavior of the un identified members. This assumption means that the precision of the completeness correction in a given bin is fixed by the number of identified cluster members in that bin. The measured
true completeness, CMIR, is the best estimate of the “true” MIR completeness CMIR associated with a spectroscopically complete cluster sample. In a bin with N cluster
true members, the expected number of detections is simply CMIRN. However, the actual true number of detections will have some range around CMIRN, which leads to an uncertainty in the inversion of CMIR to a completeness correction. This uncertainty is set by the expected variation in the number of galaxies, which is best described by binomial statistics. This allows calculation of asymmetric uncertainties on CMIR and accounts naturally for lower and upper limits. Typical uncertainties returned by this procedure are 20%. The full set of MIR completeness and the associated ∼ uncertainties are summarized in Table 3.4.
108 3.3.3. Merged Cluster Sample
I defined the MIR completeness measurements in Figure 3.12 so they apply only to galaxies with spectroscopic redshifts. Therefore, the two corrections, applied serially, give total completeness corrections for samples that rely on MIR observations. The SFG population is one such sample. The total correction XG for a galaxy G is,
1 1 XG = (3.16) Cspec(RG/R200, mR,G) × CMIR(RG/R200, fν,G)
where Cspec is the spectroscopic completeness (Figure 3.11) and CMIR is the MIR completeness (Figure 3.12). The completeness corrections described by Eqn. 3.16 can be applied to individual galaxies to extrapolate from the measured galaxy samples to the full cluster population. In cases where multiple corrections can be derived for a single object, I combine these corrections in the same way the data are combined. For example, the completeness correction for a galaxy with SFR 1/2 measurements from both 8 m and 24 m fluxes is given by X = X8µmX24µm 1/2 because SF R = SF R SF R ( 3.1.4). 8µm 24µm § To examine the dependence of star formation on environment, I construct a merged cluster galaxy sample. I identify 5 clusters (A3128, A2104, A1689, MS1008 and AC114) with the best completeness estimates and combine their members to construct this sample. The relatively small number of galaxies in A3125 results in highly irregular behavior of the completeness functions. As a result, any corrections applied to members of A3125 would depend critically on the binning scheme, so I exclude this cluster from the main sample.
109 To construct the stacked cluster, I weight individual galaxies by their completenesses. The total completeness is a combination of the spectroscopic and photometric completenesses from 3.3.1 and 3.3.2, as given by Eqns. 3.14 3.16. § §
3.4. Luminosity Functions
Luminosity functions (LFs) provide an important diagnostic for the difference between cluster galaxies and field populations, because LFs probe the entire cluster population rather than only the average. For example, Bai et al. (2009) employed the total infrared (TIR) LF to infer that RPS controls the evolution of SFRs in cluster galaxies. In this section, I discuss the derivation of total infrared (TIR) luminosities and the method I employ to construct luminosity functions.
3.4.1. Total Infrared Luminosities
The MIR observations cover a relatively narrow wavelength range from 3.6 m to 24 m. To compare my results with previous studies, I need LT IR, so I must apply bolometric corrections (BCs) determined from the measured MIR fluxes. To estimate LT IR from the Spitzer luminosities, we employ the Dale & Helou (2002) SED template library, which includes a wide variety of SEDs. These SEDs are characterized by the parameter α, which describes the intensity of the radiation field experienced by a typical dust grain.
Before I calculate BCs for IR AGN, I first subtract the AGN contribution from the dust luminosity (Paper I). I then fit each Dale & Helou (2002) template to the rest frame 5.8, 8.0 and 24 m fluxes and use the template that best fits the data
110 to measure my fiducial BCs. In the frequent cases where luminosities in one or more of these bands are unavailable, I estimate the missing luminosities from model SEDs A10. When this is necessary, the uncertainties on the fluxes come from the uncertainty on the model SED. I only calculate LT IR for galaxies with detections in at least one of the 8 m and 24 m bands. This ensures that my estimates of LT IR are dominated by dust emission rather than the Rayleigh Jeans tail of the stellar continuum.
In galaxies that have measurements of both the 8 and 24 m luminosities, I calculate LT IR separately for each band and take the geometric mean of the results. This follows the determination of SFR ( 3.1.4). In other cases, I simply use the § BC appropriate for the band with a detection. Typical BCs are 6 for L and ∼ 8µm 8 for L . I also construct 68% confidence intervals for each BC based on the ∼ 24µm χ2 = 1 interval for each galaxy. These uncertainties are asymmetric, and they add in quadrature to the uncertainties on L8 and L24 to give the total uncertainty on
LT IR.
3.4.2. Construction
The TIR luminosity functions of galaxies in the main cluster sample are derived from the L = +σu described in 3.4.1. The procedure I adopt to account for T IR −σl § the uncertainties on LT IR also reduces the sensitivity of the bright end of the LF to Poisson fluctuations in the number of luminous galaxies. I distribute the galaxy weights described in 3.3 over luminosity bins according to the probability that the § true luminosity of a galaxy with best estimate L = LT IR lies in a given bin. Due to the uncertainty on the LF prior, this technique increases the statistical uncertainty
111 on the total weight in each bin by 10%. In exchange, the much larger uncertainty ∼ introduced by stochasticity in the number of luminous galaxies shrinks.
To distribute galaxy weights over luminosity bins, I employ a probability density function (PDF) to describe the probability that a galaxy with measured
true ′ LT IR has intrinsic LT IR = L . I integrate the PDF across each luminosity bin to determine the weight in each bin. These weights add to give the total “number” of galaxies in each bin. The method described in 3.4.1 to calculate L produces § T IR +σu asymmetric uncertainties, LT IR = −σl , which requires an asymmetric probability density function (PDF) to distribute weights correctly. This PDF must reduce to the Normal distribution in the case when the upper and lower luminosity uncertainties are equal (i.e. Gaussian errors). Here, I describe a piecewise smooth function that satisfies these requirements.
First, define an effective dispersion σe = √σlσu, where σu and σl are the upper and lower uncertainties on LT IR, respectively. Then define an alternative dispersion, σ(L), which describes the instantaneous shape of the PDF at a luminosity L,
σ IF L< σ l − l |L−µ| σe +(σl σe) IF σl L< − σl − ≤ σ(L)= σe IF L = (3.17) |L−µ| σe +(σu σe) IF < L + σu σu − ≤ σ IF + σ < L u u where is the best estimate LT IR; σu and σl are the upper and lower uncertainties on , respectively. σ(L) smoothly connects the low L and high L tails of the desired distribution function. Given σ(L), I calculate the probability density for a galaxy
112 with measured luminosity at L. This probability density is given by,
1 −(µ−L)2/2σ2(L) f(L, , σu, σl)= e (3.18) √2π σ(L) where σ(L) is given by Eqn. 3.17.
The PDF described by Eqns. 3.17 and 3.18 approaches Gaussian at the high and low L extremes, with dispersions σu and σl, respectively. It also smoothly connects these two limiting cases, integrates to unity, and has dispersion equal to the mean of σl and σu at . It therefore gives a PDF for the luminosity of a given galaxy that satisfies the requirements and that is consistent with the available information about LT IR.
In addition to the PDF, the method requires a prior on the shape of the LF to correct for Eddington like bias due to the steepness of the LF above L∗. I adopt a Schechter function fit to the Coma cluster LF from Bai et al. (2006), as the baseline prior. I then correct the Coma LF to the redshift of each individual cluster according to the evolution of the field galaxy LF (Le Floc’h et al. 2005). The uncertainty on the prior adds with the statistical uncertainty on the number of galaxies in each luminosity bin to give the total uncertainty on the LF. The choice of prior has a strong impact on the bright end shape of the LF because there are few cluster galaxies to constrain the LF in this regime, so the LF produced with this technique will de emphasize differences between the observed LF and the prior relative to the intrinsic LF.
113 Fig. 3.1.— Model SEDs for galaxies hosting M06 X ray point sources. Bands shown are, in order of wavelength, B, V , R, I, [3.6], [4.5], [5.8], [8.0] and [24.0]. The panels are labeled with the names assigned by M06 in their Table 4. Objects also identified as AGN from their SED fitting are labeled “IR.” The heavy lines show the total model SED, while the solid, dotted, dashed and dot-dashed lines show the A10 AGN, elliptical, spiral and irregular templates, respectively. Few model SEDs require all 4 components. See 3.1 for further details. §
114 Fig. 3.2.— Model SEDs for galaxies hosting M06 X ray point sources. Labels and line styles are the same as in Figure 3.1.
115 Fig. 3.3.— Model SEDs for galaxies hosting M06 X ray point sources. Labels and line styles are the same as in Figure 3.1. The poor fit to AC114#5 indicates a bad spectroscopic redshift. (See 3.1.) §
116 Fig. 3.4.— Model SEDs for objects identified as IR AGN which are not also identified as X ray AGN. Line types and bandpasses shown are the same as in Figure 3.1. The object names indicated on each panel correspond to those in Table 4.1. See Section 3.1 for further details.
117 Fig. 3.5.— Model SEDs for objects identified as IR AGN which are not also identified as X ray AGN. Object names and line types are the same as in Figure 3.4.
118 Fig. 3.6.— Likelihood ratio (ρ) distributions for fits to artificial galaxies with no AGN component after photometric errors have been added. Each panel shows the distributions resulting from examining 10,000 normal galaxies. Galaxies with ρ = 1 are well fit by the three normal galaxy templates and do not require an AGN component. An object with ρ = 0 would be perfectly fit by the 4 template model 2 SED and have χgal+AGN = 0. The dashed line indicates the selection threshold, ρmax used to identify IR AGN. See Section 3.1.1 for further details.
119 Fig. 3.7.— Application of F statistics for AGN identification based on fits of galaxy only and galaxy plus AGN model SEDs to measured photometry. Red triangles show AGN identified using the ρ threshold shown in Figure 3.6 (IR AGN). Open blue squares show X ray AGN, and solid black squares show “normal” cluster members. The dotted and dashed curves show the ρ thresholds for objects having N=6 and N=9 flux measurements, respectively. Objects above their corresponding selection 2 boundaries are identified as AGN, provided that they pass a χν cut.
120 Fig. 3.8.— Comparison of SFRs determined using the IRAC and MIPS relations of Zhu et al. (2008). The solid lines in both panels denote equality. The bottom panel shows the fractional residuals between the two star formation indicators. The MIPS SFRs are biased high with respect to IRAC SFRs by 0.1 dex, which is smaller than the intrinsic scatter (0.2 dex) but still significant at 2.4σ.
121 Fig. 3.9.— Comparison of the Lbol–LX relation implied by the cluster AGN and that found by M04 (red dashed line). The measured X ray (0.3–8 keV) and bolometric luminosities of AGN identified by both the X ray and IR selection criteria are shown by black points. Lbol is derived by integrating the A10 AGN model SED component from 1216Ato1˚ m and assuming a declining continuum with F ν−2 for λ> 1 m. ν ∝ L is determined assuming a Γ = 1.7 power law. See 4.2 for further information. X §
122 Fig. 3.10.— Comparison of galaxy density (upper panel) and spectroscopic membership fraction (lower panel) as a function of radius for the clusters in the sample with enough confirmed members to make a meaningful measurement. If fspec were a good proxy for fCl,spec, the upper and lower panels would have similar slopes.
123 Fig. 3.11.— Fraction of cluster galaxies with spectra as a function of projected distance from the cluster center (left) and R band magnitude (right). The measurements in each column have been separated by the indicated variable (colored points). The black points show the completeness averaged over all of the colored bins, and the grey bands show the 1σ confidence intervals on the total completeness in each cluster. The vertical lines on the right column indicate the magnitude roughly corresponding to the MR = 20 limit. The average measurements use only galaxies with M 20 and R/R −< 0.4 (filled, black points). R ≤ − 200
124 Fig. 3.12.— MIR completeness as a function of flux for the 8 m (left) and the 24 m (right) Spitzer bands. In each column, the sample has been separated into 4 radial bins. Fluxes have not been color corrected and are given in the observer frame. Uncertainties are shown for a single radial bin to indicate typical values. MS 1008.1 1224 has no MIPS coverage. Completeness measurements are derived as described in 3.3.2. §
125 10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
a3128 001 0.063 3.2 0.7 1.9 < 0.33 0.89 ± ± a3128 002 0.058 7.1 1.5 4.3 < 0.39 0.63 ± ± a3128 003 0.060 0.3 0.1 0.2 < 0.30 2.55 ± ± a3128 004 0.063 3.2 0.9 1.9 < 0.04 0.23 ± ± a3128 005 0.062 1.6 0.3 0.9 < 0.45 1.11 ± ± a3128 006 0.059 1.1 0.2 0.7 0.7 0.2 1.19 ± ± ± a3128 007 0.059 0.6 0.1 0.4 < 0.40 2.46 ± ± a3128 008 0.057 6.2 1.3 3.7 < 0.44 2.15 ± ± a3128 009 0.055 3.4 0.7 2.0 < 0.72 0.48
126 ± ± a3128 010 0.061 0.2 0.0 0.1 1.5 0.4 2.32 ± ± ± a3128 011 0.061 0.1 0.0 0.1 < 0.30 1.23 ± ± a3128 012 0.054 1.9 0.6 1.1 < 0.35 1.20 ± ± a3128 013 0.067 2.1 0.4 1.2 0.4 0.1 2.29 ± ± ± a3128 014 0.056 1.5 0.3 0.9 < 0.28 2.21 ± ± a3128 015 0.064 0.2 0.1 0.1 < 0.25 2.42 ± ± a3128 016 0.057 9.7 2.0 5.9 0.9 0.2 2.27 ± ± ± a3128 017 0.063 4.9 1.0 3.0 < 0.33 2.74 ± ± a3128 018 0.059 0.9 0.2 0.5 3.5 0.4 2.79 ± ± ± a3128 019 0.055 5.5 1.1 3.3 < 0.30 0.54 ± ± (continued) Table 3.1. Cluster Member Summary Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
a3128 020 0.055 3.0 0.6 1.8 < 0.99 1.50 ± ± a3128 021 0.057 1.1 0.2 0.7 < 0.29 2.32 ± ± a3128 024 0.056 0.6 0.1 0.4 < 0.25 2.07 ± ± a3128 025 0.057 31.2 6.5 18.9 < 0.68 1.18 ± ± a3128 026 0.063 2.0 0.4 1.2 < 0.28 2.74 ± ± a3128 027 0.065 1.2 0.3 0.7 < 0.25 2.69 ± ± a3128 028 0.051 2.4 0.5 1.4 1.4 0.3 1.54 ± ± ± a3128 029 0.059 35.4 7.3 21.4 < 0.63 2.21
127 ± ± a3128 032 0.061 2.1 0.4 1.3 < 0.30 3.04 ± ± a3128 033 0.051 11.3 2.3 6.8 < 0.54 1.60 ± ± a3128 034 0.057 1.2 0.2 0.7 < 0.35 1.68 ± ± a3128 035 0.057 2.6 0.5 1.6 1.3 0.2 0.28 ± ± ± a3128 036 0.057 0.1 0.0 0.0 < 0.30 1.78 ± ± a3128 037 0.055 22.5 4.6 13.6 < 0.69 0.50 ± ± a3128 038 0.059 3.4 0.7 2.0 < 1.23 2.17 ± ± a3128 039 0.063 0.8 0.2 0.5 < 0.41 2.23 ± ± a3128 040 0.057 2.5 0.5 1.5 < 1.03 2.10 ± ± a3128 041 0.060 1.6 0.3 1.0 < 1.01 1.23 ± ± a3128 042 0.057 5.6 1.2 3.4 < 0.41 1.85 ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
a3128 043 0.060 0.8 0.2 0.5 1.7 0.5 1.83 ± ± ± a3128 044 0.065 20.8 4.3 12.6 1.1 0.2 2.65 ± ± ± a3128 045 0.055 0.2 0.0 0.1 < 0.78 1.69 ± ± a3128 046 0.058 2.9 0.6 1.7 < 1.45 1.28 ± ± a3128 047 0.059 3.8 0.8 2.3 < 1.01 1.12 ± ± a3128 048 0.063 1.1 0.2 0.6 < 0.31 3.46 ± ± a3128 049 0.066 3.5 0.7 2.1 < 0.34 3.43 ± ± a3128 050 0.053 5.8 1.2 3.5 2.1 0.3 3.80
128 ± ± ± a3128 051 0.054 0.3 0.1 0.2 < 1.12 2.26 ± ± a3128 053 0.062 0.1 0.0 0.1 — 1.83 ± ± a3128 054 0.058 < 0.0 < 0.52 1.60 a3128 055 0.061 < 0.0 < 0.29 1.38 a3128 056 0.064 1.0 0.2 0.6 4.8 0.5 2.48 ± ± ± a3128 057 0.058 11.4 2.4 6.9 < 0.96 1.48 ± ± a3128 060 0.062 < 0.1 < 0.28 3.64 a3128 062 0.061 2.1 0.5 1.3 — 2.23 ± ± a3128 063 0.061 18.3 3.8 11.1 1.8 0.3 4.04 ± ± ± a3128 064 0.058 2.0 0.4 1.2 < 0.56 1.27 ± ± a3128 065 0.064 26.1 5.4 15.8 < 0.66 3.25 ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
a3128 067 0.064 0.5 0.1 0.3 < 0.31 3.66 ± ± a3128 068 0.063 0.2 0.0 0.1 < 0.46 2.55 ± ± a3128 069 0.058 2.5 0.5 1.5 < 0.34 1.07 ± ± a3128 070 0.063 0.1 0.0 0.1 < 0.30 1.38 ± ± a3128 071 0.065 1.9 0.4 1.1 < 0.26 3.67 ± ± a3128 072 0.065 1.9 0.4 1.1 < 0.32 3.06 ± ± a3128 073 0.059 4.5 0.9 2.8 < 0.48 1.68 ± ± a3128 074 0.062 0.2 0.1 0.1 < 0.31 3.95
129 ± ± a3128 077 0.064 0.7 0.1 0.4 < 0.32 3.63 ± ± a3128 078 0.061 0.3 0.1 0.2 < 0.74 0.84 ± ± a3128 079 0.059 2.4 0.5 1.4 < 1.02 0.67 ± ± a3128 080 0.062 1.6 0.3 0.9 < 0.33 3.90 ± ± a3128 081 0.060 1.0 0.2 0.6 < 0.97 1.06 ± ± a3128 082 0.063 2.6 0.5 1.5 < 0.47 2.29 ± ± a3128 085 0.062 3.3 0.7 2.0 < 0.38 1.75 ± ± a3128 087 0.060 8.8 1.8 5.3 < 0.95 1.25 ± ± a3128 092 0.058 1.1 0.2 0.6 < 0.44 1.97 ± ± a3128 095 0.064 1.4 0.3 0.9 < 0.36 3.65 ± ± a3128 098 0.061 0.5 0.1 0.3 1.6 0.2 1.63 ± ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
a3128 099 0.060 12.3 2.5 7.4 1.4 0.4 1.25 ± ± ± a3128 101 0.056 1.4 0.3 0.9 1.6 0.3 1.33 ± ± ± a3128 102 0.062 5.3 1.1 3.2 < 1.31 1.81 ± ± a3128 107 0.061 0.2 0.1 0.1 < 0.92 4.08 ± ± a3128 111 0.061 0.5 0.1 0.3 < 1.43 1.36 ± ± a3128 118 0.065 5.5 1.1 3.4 < 0.70 1.05 ± ± a3125 001 0.062 3.3 0.7 2.0 5.1 0.7 3.04 ± ± ± a3125 005 0.064 1.3 0.3 0.8 < 0.85 1.41
130 ± ± a3125 008 0.063 0.8 0.2 0.5 < 0.87 1.50 ± ± a3125 011 0.064 9.1 1.9 5.5 < 0.38 1.71 ± ± a3125 012 0.065 0.1 0.0 0.1 < 0.39 2.25 ± ± a3125 013 0.063 13.3 2.8 8.1 < 0.40 1.40 ± ± a3125 014 0.058 7.3 1.5 4.4 < 0.33 0.57 ± ± a3125 015 0.063 3.8 0.8 2.3 < 0.31 1.37 ± ± a3125 016 0.060 0.0 0.0 0.0 < 1.24 1.00 ± ± a3125 017 0.059 18.0 3.7 10.9 < 0.52 1.34 ± ± a3125 018 0.061 0.1 0.0 0.1 < 0.34 3.34 ± ± a3125 021 0.060 0.7 0.1 0.4 — 1.23 ± ± a3125 023 0.063 1.3 0.3 0.8 < 0.29 1.50 ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
a3125 024 0.065 4.2 0.9 2.5 0.8 0.2 1.35 ± ± ± a3125 028 0.062 0.0 0.0 0.0 < 0.65 1.57 ± ± a3125 029 0.060 1.9 0.4 1.1 < 0.29 1.37 ± ± a3125 030 0.062 0.1 0.0 0.1 < 0.44 1.20 ± ± a3125 031 0.061 8.1 1.7 4.8 1.7 0.3 1.84 ± ± ± a3125 032 0.061 0.3 0.1 0.2 — 1.25 ± ± a3125 034 0.061 0.4 0.1 0.2 < 0.97 1.35 ± ± a3125 038 0.062 0.9 0.2 0.5 < 0.45 1.17
131 ± ± a3125 039 0.066 0.0 0.0 0.0 < 0.34 0.98 ± ± a3125 040 0.062 0.7 0.2 0.4 < 1.02 0.93 ± ± a3125 044 0.062 7.5 1.6 4.5 2.7 0.4 1.41 ± ± ± a3125 045 0.058 20.2 4.2 12.2 < 0.60 0.91 ± ± a644 005 0.071 0.1 0.0 0.1 — 1.07 ± ± a644 011 0.073 2.6 0.9 1.6 < 1.38 0.62 ± ± a644 012 0.071 0.1 0.0 0.1 < 0.72 1.05 ± ± a644 013 0.067 0.2 0.1 0.1 < 0.67 0.67 ± ± a644 017 0.081 0.1 0.0 0.0 — 0.56 ± ± a644 020 0.068 8.4 2.0 5.1 — 0.68 ± ± a644 024 0.078 7.6 1.8 4.5 1.2 0.4 0.35 ± ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
a644 025 0.072 0.7 0.2 0.4 — 0.75 ± ± a2104 001 0.153 3.1 1.0 1.9 < 0.20 1.33 ± ± a2104 002 0.150 15.5 4.9 9.3 < 0.72 0.85 ± ± a2104 003 0.154 48.8 15.0 29.4 4.0 1.2 0.85 ± ± ± a2104 004 0.150 7.0 2.2 4.2 < 0.65 0.84 ± ± a2104 005 0.163 11.1 3.5 6.7 < 0.63 0.79 ± ± a2104 006 0.158 3.7 1.2 2.2 < 0.61 0.86 ± ± a2104 007 0.158 8.8 2.8 5.3 < 0.60 2.10
132 ± ± a2104 008 0.153 14.7 4.7 8.9 < 0.62 0.40 ± ± a2104 009 0.154 5.4 1.7 3.3 < 0.51 0.46 ± ± a2104 010 0.151 < 4.3 < 0.78 2.22 a2104 011 0.150 3.0 1.0 1.8 < 0.70 1.02 ± ± a2104 012 0.150 < 7.0 < 1.53 2.07 a2104 013 0.156 29.0 9.0 17.5 < 0.72 1.44 ± ± a2104 014 0.149 10.6 3.3 6.4 < 0.61 1.12 ± ± a2104 015 0.158 3.7 1.1 2.2 2.0 0.3 0.67 ± ± ± a2104 016 0.149 2.7 0.8 1.6 < 0.49 1.89 ± ± a2104 017 0.158 18.5 5.7 11.1 < 0.82 0.94 ± ± a2104 018 0.162 7.1 2.2 4.3 < 0.55 1.58 ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
a2104 019 0.149 11.8 3.7 7.2 < 0.82 0.47 ± ± a2104 020 0.155 11.2 3.5 6.8 < 0.51 0.59 ± ± a2104 021 0.155 4.8 1.5 2.9 < 0.59 0.96 ± ± a2104 022 0.156 3.5 1.1 2.0 6.1 0.8 1.62 ± ± ± a2104 023 0.153 10.0 3.1 6.0 < 0.56 0.53 ± ± a2104 024 0.157 2.0 0.6 1.2 < 0.50 0.72 ± ± a2104 025 0.150 10.2 3.2 6.2 < 0.49 0.12 ± ± a2104 026 0.146 < 2.1 < 1.07 2.63 133 a2104 027 0.153 11.2 3.5 6.8 < 0.71 0.48 ± ± a2104 028 0.148 5.7 1.8 3.5 < 0.54 1.56 ± ± a2104 029 0.153 1.6 0.5 1.0 < 0.53 0.47 ± ± a2104 030 0.162 6.3 2.0 3.8 < 0.49 0.93 ± ± a2104 031 0.157 3.8 1.2 2.3 < 0.48 0.60 ± ± a2104 032 0.162 31.0 9.6 18.6 < 0.55 0.91 ± ± a2104 033 0.149 2.3 0.7 1.4 < 0.18 2.23 ± ± a2104 034 0.167 12.4 3.9 7.5 < 0.68 0.66 ± ± a2104 035 0.158 10.6 3.3 6.3 3.1 0.5 0.23 ± ± ± a2104 036 0.150 2.1 0.7 1.3 < 0.42 1.42 ± ± a2104 037 0.155 2.1 0.7 1.3 < 0.40 2.03 ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
a2104 038 0.166 3.1 1.0 1.9 1.1 0.3 0.70 ± ± ± a2104 039 0.155 1.3 0.4 0.7 2.5 0.8 1.17 ± ± ± a2104 040 0.154 12.4 3.8 7.4 1.9 0.6 0.74 ± ± ± a2104 041 0.156 1.4 0.4 0.8 2.0 0.4 0.79 ± ± ± a2104 042 0.166 4.7 1.5 2.8 < 0.79 0.63 ± ± a2104 043 0.165 9.3 3.0 5.6 < 0.80 1.28 ± ± a2104 044 0.164 2.3 0.7 1.4 < 0.56 1.02 ± ± a2104 045 0.160 5.2 1.6 3.1 3.1 0.4 0.47
134 ± ± ± a2104 046 0.161 1.7 0.5 1.0 < 0.83 1.66 ± ± a2104 047 0.159 8.5 2.6 5.1 13.0 1.6 0.15 ± ± ± a2104 048 0.152 2.9 0.9 1.7 < 0.65 0.46 ± ± a2104 049 0.154 7.9 2.5 4.8 < 1.13 0.58 ± ± a2104 050 0.157 4.9 1.5 2.9 < 0.68 1.94 ± ± a2104 051 0.155 < 2.3 < 16300.00 0.19 a2104 052 0.160 3.8 1.2 2.3 4.5 0.6 1.95 ± ± ± a2104 053 0.150 < 0.3 < 1.31 1.20 a2104 054 0.161 6.0 1.9 3.6 < 0.52 0.23 ± ± a2104 055 0.161 6.6 2.1 4.0 < 0.63 1.28 ± ± a2104 056 0.148 < 0.9 — 0.65
(continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
a2104 057 0.149 7.4 2.3 4.5 < 0.52 0.94 ± ± a2104 059 0.146 7.7 2.4 4.6 < 0.68 1.49 ± ± a2104 061 0.156 0.6 0.2 0.3 3.0 0.4 2.42 ± ± ± a2104 062 0.159 6.1 1.9 3.6 2.5 0.5 0.27 ± ± ± a2104 063 0.144 < 1.1 < 0.70 1.72 a2104 064 0.152 19.8 6.1 12.0 1.3 0.3 1.15 ± ± ± a2104 065 0.155 2.0 0.6 1.2 < 0.58 1.08 ± ± a2104 067 0.150 12.9 4.0 7.8 0.8 0.2 1.61
135 ± ± ± a2104 069 0.149 7.1 2.2 4.3 < 0.91 1.13 ± ± a2104 070 0.152 < 1.1 — 0.55 a2104 072 0.154 < 1.6 < 1.61 1.61 a2104 073 0.150 < 0.2 < 7.78 1.70 a2104 074 0.158 15.3 4.7 9.1 10.1 1.3 1.88 ± ± ± a2104 075 0.159 < 8.1 < 16400.00 1.27 a2104 076 0.162 18.4 5.7 11.0 0.5 0.1 0.88 ± ± ± a2104 077 0.161 9.2 2.8 5.5 3.9 0.7 1.08 ± ± ± a2104 079 0.155 12.8 3.9 7.6 19.2 2.6 0.58 ± ± ± a1689 004 0.183 67.1 18.2 22.9 < 1.22 1.47 ± ± a1689 006 0.175 62.5 17.0 21.0 1.7 0.4 1.09 ± ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
a1689 008 0.191 19.3 5.3 6.9 < 0.48 2.01 ± ± a1689 012 0.177 9.7 2.6 3.9 6.7 1.0 2.21 ± ± ± a1689 014 0.175 13.3 3.6 4.6 0.4 0.1 2.23 ± ± ± a1689 015 0.204 41.8 11.4 15.1 1.1 0.3 1.66 ± ± ± a1689 017 0.176 13.3 3.6 4.6 < 0.49 2.23 ± ± a1689 021 0.171 23.4 6.4 8.4 < 0.48 0.44 ± ± a1689 022 0.191 10.7 2.9 3.9 0.4 0.1 1.50 ± ± ± a1689 023 0.189 16.7 4.6 5.6 < 0.55 0.82
136 ± ± a1689 026 0.180 14.2 3.9 4.5 0.4 0.1 1.38 ± ± ± a1689 027 0.202 43.7 11.9 14.6 < 1.00 1.34 ± ± a1689 028 0.186 35.0 9.8 11.2 — 0.55 ± ± a1689 030 0.187 34.7 9.4 11.2 0.8 0.3 0.55 ± ± ± a1689 031 0.183 5.9 1.6 2.0 0.2 0.1 2.33 ± ± ± a1689 036 0.195 4.6 1.3 1.7 < 0.17 2.84 ± ± a1689 038 0.195 17.2 4.7 6.0 < 0.48 0.63 ± ± a1689 039 0.193 3.4 0.9 1.4 0.2 0.1 1.07 ± ± ± a1689 041 0.166 2.7 0.7 1.0 < 0.16 1.61 ± ± a1689 042 0.201 43.8 11.9 14.7 < 0.97 1.38 ± ± a1689 043 0.193 6.7 1.9 2.5 < 0.31 1.68 ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
a1689 045 0.191 16.6 4.5 5.6 3.1 0.5 2.42 ± ± ± a1689 049 0.186 2.1 0.6 0.8 < 0.14 1.19 ± ± a1689 050 0.180 3.0 0.8 1.1 < 0.16 2.02 ± ± a1689 052 0.180 3.9 1.1 2.0 5.4 0.7 1.95 ± ± ± a1689 055 0.190 5.8 1.6 2.0 < 0.19 2.04 ± ± a1689 058 0.177 8.1 2.2 3.0 0.3 0.1 1.32 ± ± ± a1689 059 0.200 < 0.7 < 1.00 1.38 a1689 060 0.200 2.4 0.7 1.1 < 0.16 1.10
137 ± ± a1689 061 0.196 0.3 0.1 0.2 < 0.14 1.27 ± ± a1689 062 0.185 3.4 0.9 1.2 < 0.15 0.14 ± ± a1689 064 0.185 3.2 0.9 1.3 < 0.16 0.57 ± ± a1689 065 0.189 3.2 0.9 1.3 < 0.16 0.57 ± ± a1689 067 0.185 1.0 0.3 0.4 < 0.16 0.52 ± ± a1689 069 0.191 5.7 1.6 2.0 < 0.18 0.68 ± ± a1689 070 0.175 5.2 1.4 1.9 < 0.18 0.82 ± ± a1689 071 0.175 5.3 1.4 2.7 7.5 0.9 0.35 ± ± ± a1689 072 0.182 2.4 0.7 0.9 < 0.15 1.12 ± ± a1689 074 0.196 2.3 0.6 0.9 2.5 0.4 1.62 ± ± ± a1689 076 0.197 8.8 2.4 3.3 < 0.30 0.71 ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
a1689 077 0.185 6.0 1.6 2.3 < 0.20 0.81 ± ± a1689 078 0.195 3.1 0.8 1.3 < 0.24 1.33 ± ± a1689 079 0.186 9.7 2.6 3.3 0.3 0.1 1.34 ± ± ± a1689 080 0.184 6.9 1.9 2.3 0.3 0.1 1.91 ± ± ± a1689 083 0.184 7.1 1.9 2.6 < 0.21 1.10 ± ± a1689 085 0.190 < 0.6 < 282000.00 0.27 a1689 086 0.180 1.7 0.5 0.6 < 0.15 0.84 ± ± a1689 087 0.176 0.3 0.1 0.2 1.3 0.1 1.79
138 ± ± ± a1689 088 0.179 0.4 0.1 0.2 0.6 0.1 0.67 ± ± ± a1689 092 0.193 0.8 0.2 0.3 < 0.15 0.51 ± ± a1689 093 0.191 4.6 1.3 1.8 < 0.21 0.35 ± ± a1689 094 0.178 < 0.2 3.5 1.1 0.24 ± a1689 095 0.183 10.6 2.9 4.1 < 0.29 0.83 ± ± a1689 096 0.196 3.8 1.1 1.8 3.9 0.6 1.70 ± ± ± a1689 097 0.177 0.9 0.2 0.4 < 0.16 0.13 ± ± a1689 099 0.179 1.2 0.3 0.4 < 0.18 1.84 ± ± a1689 100 0.189 < 0.1 < 0.16 2.08 a1689 103 0.197 6.2 1.7 2.9 18.7 2.1 1.68 ± ± ± a1689 105 0.183 6.8 1.9 2.2 < 0.23 1.07 ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
a1689 106 0.183 < 0.3 2.4 0.4 0.45 ± a1689 107 0.175 < 0.1 < 0.13 1.69 a1689 109 0.210 < 0.1 < 24400.00 1.39 a1689 110 0.174 0.2 0.1 0.2 < 0.26 0.91 ± ± a1689 111 0.214 < 0.3 < 0.18 0.61 a1689 112 0.188 1.5 0.4 0.9 15.3 1.8 0.32 ± ± ± a1689 113 0.198 1.1 0.3 0.5 < 0.20 1.65 ± ± a1689 114 0.182 2.7 0.7 1.4 0.4 0.1 0.36
139 ± ± ± a1689 115 0.198 0.2 0.1 0.1 0.3 0.1 0.13 ± ± ± a1689 117 0.191 5.4 1.5 1.9 < 0.27 1.00 ± ± a1689 118 0.191 11.4 3.1 3.9 1.6 0.2 1.45 ± ± ± a1689 119 0.184 8.6 2.3 3.0 < 0.25 0.83 ± ± a1689 120 0.200 5.4 1.5 2.0 < 0.27 0.49 ± ± a1689 121 0.188 1.8 0.5 0.6 < 0.19 1.40 ± ± a1689 122 0.176 4.9 1.3 1.8 < 0.29 0.87 ± ± a1689 123 0.194 < 0.0 < 0.18 0.56 a1689 124 0.179 16.2 4.4 5.6 < 0.42 0.18 ± ± a1689 126 0.178 < 0.0 < 0.18 1.94 a1689 127 0.191 0.7 0.2 0.3 < 0.17 1.07 ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
a1689 128 0.192 2.1 0.6 0.8 < 0.29 0.85 ± ± a1689 129 0.189 2.9 0.8 1.1 < 0.24 1.72 ± ± a1689 130 0.176 3.4 0.9 1.5 2.3 0.2 1.95 ± ± ± a1689 131 0.178 < 0.0 < 0.37 0.41 a1689 132 0.180 7.7 2.2 0.4 < 0.23 0.32 ± ± a1689 133 0.176 0.5 0.1 0.3 2.2 0.2 0.69 ± ± ± a1689 135 0.171 < 0.0 < 0.25 1.90 a1689 136 0.189 12.4 3.4 4.5 < 0.60 0.96
140 ± ± a1689 138 0.198 5.7 1.6 2.0 0.8 0.2 1.36 ± ± ± a1689 139 0.179 7.7 2.1 2.8 < 0.40 0.25 ± ± a1689 140 0.199 3.7 1.0 1.4 < 0.61 0.86 ± ± a1689 141 0.193 6.6 1.8 2.3 < 0.63 1.12 ± ± a1689 142 0.181 2.1 0.6 1.0 10.9 1.4 2.61 ± ± ± a1689 143 0.183 18.2 4.9 6.2 < 0.46 1.07 ± ± a1689 144 0.186 10.0 2.7 3.4 < 0.31 1.91 ± ± a1689 145 0.167 < 0.1 < 0.41 1.75 a1689 147 0.185 5.5 1.5 2.0 < 0.81 0.17 ± ± a1689 148 0.167 < 0.2 < 0.34 1.48 a1689 149 0.186 12.1 3.3 3.9 0.9 0.2 1.61 ± ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
a1689 150 0.176 5.8 1.6 2.0 < 0.35 1.75 ± ± a1689 151 0.207 4.0 1.1 1.4 1.7 0.5 0.61 ± ± ± a1689 153 0.167 1.2 0.4 0.7 < 3.35 0.71 ± ± a1689 155 0.190 0.2 0.1 0.1 < 0.28 2.02 ± ± a1689 156 0.185 < 0.0 < 0.49 0.80 a1689 158 0.179 15.0 3.4 9.0 < 1.10 2.18 ± ± a1689 159 0.192 < 0.1 < 0.89 0.72 a1689 160 0.178 12.7 2.9 7.6 < 1.11 1.75
141 ± ± a1689 161 0.188 5.5 1.2 3.3 < 0.46 2.86 ± ± a1689 162 0.204 2.3 0.6 1.4 < 1.00 0.37 ± ± a1689 163 0.183 1.2 0.3 0.7 — 0.49 ± ± a1689 164 0.195 4.5 0.9 2.6 7.8 1.1 0.35 ± ± ± a1689 165 0.174 2.3 0.5 1.4 < 0.39 0.90 ± ± a1689 166 0.179 2.1 0.5 1.3 < 1.10 1.12 ± ± a1689 167 0.184 11.3 2.6 6.8 < 1.07 0.62 ± ± a1689 168 0.175 18.5 4.0 11.1 < 0.49 1.39 ± ± a1689 170 0.196 3.4 0.8 2.0 < 0.72 1.80 ± ± a1689 171 0.208 0.5 0.1 0.3 < 0.43 0.93 ± ± a1689 172 0.185 1.1 0.3 0.7 < 1.07 1.00 ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
a1689 173 0.185 < 0.2 < 0.59 2.28 a1689 174 0.183 < 0.1 < 0.18 1.16 a1689 175 0.206 < 0.0 — 0.29 a1689 177 0.198 < 0.6 < 2.38 0.68 a1689 178 0.201 < 0.0 < 0.13 1.74 a1689 179 0.193 < 0.4 < 0.58 1.85 a1689 185 0.182 0.8 0.3 0.3 — 1.13 ± ± a1689 186 0.187 26.6 7.4 8.7 < 1.13 2.71
142 ± ± a1689 187 0.189 0.6 0.2 0.4 3.1 0.4 1.93 ± ± ± a1689 189 0.201 63.9 17.7 4.8 < 1.16 0.87 ± ± a1689 191 0.197 < 0.1 < 1.25 1.45 a1689 192 0.214 < 1.1 < 1.10 0.35 a1689 194 0.180 < 1.6 — 0.93 a1689 195 0.165 1.4 0.4 0.7 4.1 0.7 1.61 ± ± ± a1689 196 0.190 2.5 0.7 0.9 < 0.45 2.01 ± ± a1689 198 0.212 < 0.1 < 1.50 1.29 a1689 200 0.187 < 37.4 < 1.19 1.86 a1689 201 0.181 < 0.3 < 1.19 1.82 a1689 204 0.214 0.7 0.2 0.4 3.2 0.6 0.98 ± ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
a1689 207 0.179 0.3 0.1 0.2 0.6 0.2 2.56 ± ± ± a1689 209 0.184 12.0 3.3 4.3 < 1.11 1.65 ± ± a1689 211 0.176 < 0.1 < 1.65 1.71 a1689 215 0.191 5.9 1.7 0.4 < 1.12 2.25 ± ± a1689 217 0.181 < 4.0 < 16.10 1.00 a1689 218 0.196 2.8 0.8 1.8 7.0 0.9 1.11 ± ± ± a1689 219 0.178 2.3 0.7 0.8 < 1.13 1.29 ± ± a1689 220 0.181 < 0.1 < 1.24 1.82 143 a1689 221 0.187 1.7 0.5 0.7 < 0.75 0.82 ± ± a1689 229 0.193 < 0.5 < 1.40 1.44 a1689 233 0.184 6.3 1.9 2.3 — 0.96 ± ± a1689 234 0.192 < 0.7 — 1.56 a1689 238 0.184 12.6 3.4 4.6 4.2 1.0 0.89 ± ± ± a1689 244 0.184 1.4 0.4 0.7 5.4 0.9 0.67 ± ± ± a1689 251 0.187 22.6 7.2 7.7 — 0.24 ± ± a1689 252 0.185 < 1.8 12.8 2.9 0.44 ± a2163 001 0.200 < 1.4 < 0.50 1.99 a2163 002 0.201 < 4.1 28.7 3.9 1.98 ± a2163 003 0.210 < 0.1 < 0.50 1.11
(continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
a2163 004 0.194 < 0.7 < 1.42 1.95 a2163 005 0.207 < 5.3 < 1.53 1.65 a2163 006 0.193 < 4.1 < 2.29 1.14 a2163 008 0.215 < 0.4 < 3.84 0.90 a2163 013 0.199 < 0.3 < 3.10 1.32 a2163 015 0.205 < 3.6 < 2.61 1.27 a2163 016 0.208 < 2.6 — 1.15 a2163 019 0.204 < 0.2 — 1.94 144 a2163 030 0.192 < 3.8 < 5.26 2.03 a2163 039 0.199 < 0.6 < 0.78 0.54 a2163 051 0.193 < 3.4 < 1.41 0.99 a2163 060 0.212 < 2.4 — 1.30 a2163 075 0.201 < 1.8 < 1.51 0.46 a2163 088 0.197 < 0.6 5.0 1.1 0.28 ± a2163 091 0.201 < 1.1 2.5 0.8 1.57 ± a2163 093 0.199 < 0.4 — 0.85 a2163 094 0.210 < 3.5 < 1.59 0.77 a2163 096 0.200 < 33.3 < 1.28 0.50 a2163 097 0.197 < 0.8 < 1.54 0.19
(continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
a2163 098 0.195 < 1.0 < 1.95 1.64 a2163 101 0.206 < 0.2 < 2.11 2.25 a2163 109 0.199 < 0.9 < 1.20 0.65 a2163 110 0.204 < 2.2 < 1.74 0.90 a2163 111 0.203 < 6.7 < 1.50 1.11 ms1008 001 0.308 5.3 1.7 1.6 < 0.87 0.81 ± ± ms1008 002 0.316 < 0.3 < 0.84 1.39 ms1008 003 0.307 7.6 2.4 2.2 < 0.85 0.83
145 ± ± ms1008 004 0.306 170.0 48.6 48.5 < 6.67 1.06 ± ± ms1008 005 0.301 24.6 7.1 8.4 < 2.18 1.31 ± ± ms1008 006 0.313 22.4 6.4 7.9 < 1.73 1.73 ± ± ms1008 007 0.308 5.7 1.7 2.2 < 1.01 0.65 ± ± ms1008 008 0.309 < 7.0 < 1040000.00 0.23 ms1008 009 0.293 2.4 0.7 1.2 9.2 1.0 0.91 ± ± ± ms1008 010 0.304 14.6 4.4 4.4 < 0.86 0.85 ± ± ms1008 011 0.300 12.5 3.7 4.0 < 0.75 2.27 ± ± ms1008 012 0.307 5.5 1.7 1.9 < 0.85 0.59 ± ± ms1008 013 0.303 1.4 0.5 0.6 < 0.84 0.83 ± ± ms1008 014 0.306 9.2 2.7 3.4 < 0.88 0.83 ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
ms1008 015 0.315 2.7 0.8 1.6 17.5 1.4 1.61 ± ± ± ms1008 016 0.303 1.9 0.6 1.0 < 0.82 0.78 ± ± ms1008 017 0.308 2.2 0.7 0.9 < 0.83 1.14 ± ± ms1008 018 0.316 3.8 1.1 1.5 < 0.68 1.57 ± ± ms1008 019 0.304 8.9 2.7 2.9 < 0.71 0.53 ± ± ms1008 020 0.300 3.4 1.1 1.2 < 0.78 0.94 ± ± ms1008 021 0.305 2.1 0.7 0.9 < 0.87 0.89 ± ± ms1008 022 0.306 14.5 4.2 4.7 < 0.82 1.16
146 ± ± ms1008 023 0.305 7.2 2.2 2.2 < 0.84 0.46 ± ± ms1008 024 0.306 3.3 1.0 1.2 < 0.83 0.52 ± ± ms1008 025 0.314 5.1 1.5 1.8 < 0.83 2.19 ± ± ms1008 029 0.313 6.0 1.8 2.3 < 0.82 0.44 ± ± ms1008 030 0.303 5.5 1.6 2.4 8.0 1.1 1.65 ± ± ± ms1008 031 0.300 6.5 1.9 2.7 7.9 1.1 1.17 ± ± ± ms1008 033 0.308 24.2 6.9 8.0 < 1.11 1.53 ± ± ms1008 034 0.307 1.7 0.5 0.9 6.9 1.0 1.02 ± ± ± ms1008 035 0.309 25.6 5.8 15.3 5.6 1.1 0.48 ± ± ± ms1008 036 0.314 2.5 0.8 0.9 < 0.59 1.14 ± ± ms1008 037 0.302 < 0.0 < 0.89 1.36
(continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
ms1008 038 0.304 < 0.1 < 0.84 1.71 ms1008 039 0.308 13.2 3.8 4.4 < 1.77 0.23 ± ± ms1008 040 0.304 2.8 0.9 0.9 < 0.58 1.57 ± ± ms1008 041 0.300 2.9 0.9 1.2 < 0.61 1.88 ± ± ms1008 043 0.297 7.1 2.1 2.6 < 0.66 0.90 ± ± ms1008 044 0.306 16.0 4.6 6.5 4.0 0.8 1.59 ± ± ± ms1008 045 0.308 3.4 1.0 1.4 < 0.61 2.04 ± ± ms1008 046 0.297 1.8 0.5 0.9 6.9 0.8 1.20
147 ± ± ± ms1008 048 0.313 < 6.0 < 1110000.00 0.70 ms1008 049 0.305 8.0 2.4 2.6 < 0.85 0.53 ± ± ms1008 050 0.307 3.8 1.2 1.4 < 0.88 0.77 ± ± ms1008 051 0.313 5.0 1.5 1.7 2.0 0.5 1.23 ± ± ± ms1008 052 0.300 12.1 3.7 4.1 < 0.64 0.89 ± ± ms1008 053 0.307 5.4 1.6 2.5 < 1.26 1.47 ± ± ms1008 055 0.304 9.2 2.8 2.9 < 0.86 1.41 ± ± ms1008 056 0.312 52.6 15.0 18.4 < 2.54 1.71 ± ± ms1008 057 0.308 13.7 4.2 3.9 < 1.14 0.36 ± ± ms1008 058 0.300 5.5 1.6 2.4 1.3 0.4 1.15 ± ± ± ms1008 059 0.308 15.9 4.8 4.9 < 1.27 1.29 ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
ms1008 060 0.305 5.6 1.7 1.8 < 0.78 1.88 ± ± ms1008 062 0.310 6.1 1.9 2.0 < 1.32 1.25 ± ± ms1008 063 0.312 < 0.1 2.2 0.5 0.52 ± ms1008 064 0.310 2.7 0.9 1.3 < 1.53 0.45 ± ± ms1008 066 0.307 19.7 5.8 6.2 3.4 1.2 0.98 ± ± ± ms1008 068 0.301 < 0.2 < 1.41 0.94 ms1008 070 0.311 11.2 3.4 3.7 < 1.14 0.53 ± ± ms1008 075 0.309 5.2 1.6 1.6 < 0.79 1.26
148 ± ± ms1008 076 0.308 7.0 2.1 2.4 < 0.85 2.35 ± ± ms1008 078 0.308 6.4 1.9 2.0 < 0.88 0.75 ± ± ms1008 085 0.310 2.2 0.7 0.8 < 0.81 0.03 ± ± ms1008 087 0.311 3.5 1.1 1.4 < 1.57 1.08 ± ± ms1008 089 0.298 6.4 1.9 2.5 10.0 0.9 0.25 ± ± ± ms1008 094 0.311 6.1 1.8 2.0 < 0.85 2.56 ± ± ms1008 095 0.306 < 0.4 < 0.79 0.48 ms1008 096 0.305 < 0.8 < 0.89 0.47 ac114 001 0.303 < 0.1 < 0.39 1.20 ac114 002 0.330 2.5 0.7 1.3 4.3 0.7 0.35 ± ± ± ac114 003 0.310 12.7 3.7 3.5 < 0.65 0.11 ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
ac114 004 0.313 12.0 3.3 4.1 7.9 1.1 1.07 ± ± ± ac114 005 0.333 < 1.3 < 0.65 0.91 ac114 006 0.320 17.9 5.0 4.7 3.2 0.5 0.70 ± ± ± ac114 007 0.327 < 0.0 < 0.66 1.08 ac114 008 0.317 8.3 2.5 2.4 < 0.65 0.91 ± ± ac114 009 0.313 < 1.9 < 0.66 1.13 ac114 010 0.313 4.6 1.4 1.4 < 0.58 2.29 ± ± ac114 011 0.317 < 0.8 < 0.59 3.09 149 ac114 012 0.314 < 1.9 < 0.62 1.85 ac114 014 0.332 < 2.8 < 0.59 2.58 ac114 015 0.330 < 0.2 < 0.64 1.67 ac114 016 0.312 30.6 8.5 6.9 < 1.01 1.46 ± ± ac114 017 0.306 6.8 2.0 2.0 < 0.69 1.32 ± ± ac114 018 0.322 13.4 3.8 3.6 < 0.64 0.91 ± ± ac114 019 0.306 < 0.3 < 0.68 0.69 ac114 020 0.324 27.9 7.8 6.5 < 0.89 1.01 ± ± ac114 021 0.312 12.1 3.4 3.8 10.9 1.3 0.92 ± ± ± ac114 022 0.319 < 1.0 < 0.59 0.75 ac114 023 0.312 2.5 0.7 1.2 1.6 0.3 1.50 ± ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
ac114 025 0.316 < 0.4 < 0.66 2.05 ac114 026 0.311 15.1 4.2 4.2 < 1.10 1.23 ± ± ac114 028 0.335 1.7 0.5 1.0 2.9 0.5 0.87 ± ± ± ac114 029 0.312 1.8 0.6 0.9 < 0.38 0.86 ± ± ac114 030 0.321 11.8 3.4 3.0 < 0.64 1.63 ± ± ac114 031 0.316 < 0.8 < 0.59 1.86 ac114 032 0.313 < 6.1 < 0.69 1.75 ac114 033 0.317 207.0 57.0 38.4 9.5 2.7 0.73
150 ± ± ± ac114 035 0.322 8.7 2.6 2.2 < 0.63 1.59 ± ± ac114 036 0.331 11.8 3.3 3.6 4.8 0.8 1.02 ± ± ± ac114 037 0.321 < 0.2 < 0.97 0.83 ac114 038 0.319 7.7 2.3 2.1 < 0.65 1.58 ± ± ac114 039 0.307 < 1.0 < 0.65 2.85 ac114 040 0.309 11.3 3.4 2.5 < 0.61 1.33 ± ± ac114 041 0.311 < 13.8 < 1.25 0.94 ac114 042 0.322 18.5 5.2 5.1 < 0.94 0.79 ± ± ac114 043 0.307 < 6.4 < 0.60 1.56 ac114 044 0.316 < 0.1 — 0.19 ac114 045 0.316 13.0 3.9 3.0 < 0.66 3.29 ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
ac114 046 0.329 < 1.2 < 0.59 0.81 ac114 048 0.312 7.6 2.3 2.0 < 0.59 1.81 ± ± ac114 049 0.305 1.7 0.5 0.9 < 0.34 2.22 ± ± ac114 050 0.302 7.6 2.3 2.0 < 0.64 1.51 ± ± ac114 051 0.322 3.9 1.3 1.1 < 0.57 1.41 ± ± ac114 052 0.325 13.0 3.7 3.9 < 1.78 1.30 ± ± ac114 053 0.321 < 6.5 < 1.03 0.63 ac114 054 0.326 < 1.3 < 0.64 0.90 151 ac114 055 0.326 < 0.7 < 0.62 0.89 ac114 056 0.326 < 1.4 < 0.61 3.86 ac114 058 0.325 4.1 1.4 1.2 < 0.61 1.07 ± ± ac114 059 0.325 < 0.9 < 0.60 1.16 ac114 060 0.315 < 1.2 < 0.61 3.19 ac114 061 0.301 < 1.4 < 0.41 1.72 ac114 062 0.311 6.6 1.9 2.2 < 0.65 1.48 ± ± ac114 063 0.309 < 2.5 < 0.65 0.66 ac114 064 0.319 5.1 1.6 1.3 < 0.34 1.22 ± ± ac114 065 0.312 < 0.9 < 0.60 0.46 ac114 066 0.309 70.6 19.4 16.0 < 1.95 0.63 ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
ac114 067 0.321 < 1.1 < 0.57 0.71 ac114 068 0.308 13.0 3.6 3.4 < 0.76 1.18 ± ± ac114 069 0.324 6.9 2.1 2.4 < 1.19 1.87 ± ± ac114 070 0.325 < 1.1 4.2 0.9 1.91 ± ac114 071 0.313 9.4 2.6 2.7 < 0.77 2.61 ± ± ac114 072 0.325 < 0.8 < 1.91 1.28 ac114 073 0.291 < 2.2 < 0.57 2.19 ac114 074 0.312 16.2 4.5 3.9 < 0.66 0.56
152 ± ± ac114 075 0.312 13.0 3.7 3.0 < 0.61 0.85 ± ± ac114 076 0.311 < 0.3 < 0.58 3.58 ac114 077 0.314 8.1 2.4 2.5 < 0.63 1.61 ± ± ac114 078 0.315 3.2 1.1 1.0 < 0.57 0.82 ± ± ac114 081 0.308 43.8 12.1 10.0 < 1.29 0.60 ± ± ac114 082 0.324 < 1.1 < 0.59 0.45 ac114 083 0.320 19.0 5.4 4.2 < 1.04 2.74 ± ± ac114 084 0.321 < 0.8 < 0.58 1.67 ac114 086 0.301 1.2 0.4 0.7 6.6 0.8 2.56 ± ± ± ac114 089 0.326 14.3 4.0 3.5 < 0.60 1.52 ± ± ac114 090 0.319 < 5.8 < 1.28 2.25
(continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
ac114 091 0.308 4.3 1.4 1.5 < 0.67 0.79 ± ± ac114 092 0.306 < 0.4 < 0.86 1.35 ac114 093 0.319 16.3 4.6 4.5 < 1.20 1.59 ± ± ac114 094 0.326 6.9 2.1 2.2 < 0.59 0.46 ± ± ac114 095 0.331 25.1 6.9 7.5 1.7 0.5 1.25 ± ± ± ac114 099 0.319 < 1.1 — 1.67 ac114 100 0.325 1.0 0.3 0.7 4.0 0.8 0.88 ± ± ± ac114 102 0.330 < 0.2 < 0.59 1.36 153 ac114 103 0.298 < 1.0 < 0.59 0.91 ac114 105 0.325 < 1.6 < 1.18 0.16 ac114 106 0.313 < 0.7 3.9 0.6 1.12 ± ac114 107 0.307 30.3 8.4 6.7 < 1.02 0.87 ± ± ac114 110 0.318 < 1.0 < 0.67 0.78 ac114 111 0.299 < 0.1 < 0.70 2.29 ac114 112 0.320 1.6 0.5 0.9 3.8 0.6 0.69 ± ± ± ac114 114 0.309 3.7 1.2 1.1 < 0.64 0.71 ± ± ac114 115 0.318 107.0 30.9 22.6 1.5 0.5 1.69 ± ± ± ac114 116 0.310 4.9 1.5 2.0 26.9 3.2 0.79 ± ± ± ac114 118 0.314 5.0 1.4 1.7 2.0 0.5 0.57 ± ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
ac114 119 0.314 < 1.2 < 0.62 0.21 ac114 120 0.328 5.0 1.6 3.0 < 0.00 0.65 ± ± ac114 121 0.318 7.0 2.2 1.9 < 0.42 2.74 ± ± ac114 122 0.327 < 0.0 < 0.44 0.74 ac114 123 0.307 7.9 2.2 2.7 8.2 1.2 2.65 ± ± ± ac114 124 0.313 2.4 0.7 1.2 6.8 1.0 1.01 ± ± ± ac114 126 0.318 29.1 8.1 6.9 < 1.89 1.11 ± ± ac114 127 0.314 37.5 10.3 11.0 3.6 0.7 1.36
154 ± ± ± ac114 129 0.318 4.7 1.4 1.5 < 0.50 0.40 ± ± ac114 130 0.321 < 0.4 < 0.60 0.55 ac114 131 0.308 < 1.1 < 0.84 1.18 ac114 132 0.308 11.1 3.2 3.0 < 0.59 0.87 ± ± ac114 135 0.306 < 4.0 < 1.21 1.52 ac114 137 0.326 < 363.0 < 0.60 1.01 ac114 138 0.307 0.9 0.3 0.7 9.3 1.2 2.01 ± ± ± ac114 139 0.306 8.5 2.4 2.5 15.1 1.9 1.21 ± ± ± ac114 140 0.312 24.8 7.2 6.2 < 1.10 2.07 ± ± ac114 141 0.300 < 0.5 < 1.24 2.92 ac114 142 0.325 < 0.3 2.1 0.7 0.84 ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
ac114 143 0.305 < 0.3 < 0.95 0.10 ac114 146 0.303 < 1.0 < 1.06 3.05 ac114 147 0.306 < 0.1 < 0.91 1.12 ac114 149 0.318 17.7 5.0 5.5 30.3 3.5 1.62 ± ± ± ac114 150 0.315 10.9 3.1 3.2 3.8 0.6 2.88 ± ± ± ac114 152 0.315 4.9 1.5 1.6 < 1.16 3.60 ± ± ac114 153 0.315 < 0.2 < 0.71 2.11 ac114 154 0.325 < 0.9 < 0.75 1.48 155 ac114 155 0.324 < 0.8 5.7 1.0 1.17 ± ac114 156 0.314 2.8 0.8 1.3 7.3 1.0 2.01 ± ± ± ac114 157 0.326 < 0.3 < 1.20 2.16 ac114 160 0.327 < 0.5 < 0.71 0.79 ac114 161 0.303 < 4.9 < 1.63 1.54 ac114 162 0.322 < 0.9 < 0.93 1.00 ac114 163 0.318 6.9 2.0 2.5 8.3 1.2 0.71 ± ± ± ac114 164 0.326 1.4 0.5 0.7 2.1 0.6 0.35 ± ± ± ac114 165 0.326 < 0.7 < 1.19 0.76 ac114 166 0.313 < 0.1 — 1.15 ac114 167 0.307 7.7 2.4 2.9 27.6 4.6 0.25 ± ± ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
ac114 169 0.297 28.8 8.3 6.0 < 1.22 2.83 ± ± ac114 170 0.306 3.2 1.0 1.3 10.8 1.5 2.02 ± ± ± ac114 174 0.317 < 0.2 2.4 0.7 0.76 ± ac114 176 0.323 < 4.7 < 2.02 1.95 ac114 177 0.319 < 2.6 — 0.88 ac114 178 0.317 12.0 3.5 5.0 41.1 4.9 0.30 ± ± ± ac114 181 0.315 < 1.2 < 1.22 1.20
156 ac114 182 0.301 < 2.9 < 1.57 1.69 ac114 185 0.303 < 0.4 < 0.80 1.13 ac114 188 0.316 4.4 1.3 1.9 14.8 2.0 1.79 ± ± ± ac114 190 0.296 < 1.5 < 0.80 0.65 ac114 191 0.306 < 0.5 < 2.14 0.87 ac114 192 0.322 < 0.4 3.5 0.6 1.95 ± ac114 193 0.299 < 0.2 < 0.79 0.79 ac114 196 0.327 < 0.3 < 0.82 2.90 ac114 197 0.322 5.2 1.5 2.8 18.3 2.5 1.94 ± ± ± ac114 199 0.321 < 0.4 3.7 0.8 2.06 ± (continued) Table 3.1—Continued
10 −1 Name z M∗ [10 M⊙] SFR [M⊙ yr ] δ (1) (2) (3) (4) (5)
ac114 202 0.306 < 0.1 < 0.83 0.92
157 Note. — The properties of cluster member galaxies, determined using the methods described by Atlee et al. (2011). (1) The name of this object, which is identical to the name given in Table 2.3. (2) Redshifts of each object, as determined by Martini et al. (2006, 2007) where available, or from the literature otherwise. (3) Stellar masses derived using mass to light ratios appropriate for each galaxy’s color and assuming a scaled Salpeter IMF with Bruzual & Charlot population synthesis model (Bell & de Jong 2001, Table 4). The first uncertainty quoted gives the statistical error, and the second gives the systematic error. (4) SFRs from the 8 m luminosity, the 24 m luminosity or the geometric mean of the two. Uncertainties include only statistical errors, and upper limits are quoted at 3σ in the more sensitive of the 8 m and 24 m bands. (5) Substructure parameter of Dressler & Shectman (1988), δ = (11/σ2) ( v v )2 +(σ σ)2 , where the local average velocities ( v) and velocity dispersions (σ) × local − local − are calculated over the 10 nearest neighbors of each galaxy. LAGN : 100 1.0 1.0 0.4 0.4 0.2 0.2 0.8 0.8 1.5 1.5 100 − → − − → − − → → → → E(B V) − 0–0.075 6.9 0.3% 8.1 0.3% 17.8 0.4% 45.5 0.9% 72.9 1.9% 83.8 4.6% ± ± ± ± ± ± 0.075–0.15 7.0 0.4% 8.3 0.3% 17.8 0.4% 45.6 0.6% 75.5 2.0%% 83.8 5.0% ± ± ± ± ± ± 0.15–0.3 7.0 0.3% 8.3 0.3% 17.1 0.4% 44.7 0.8% 74.2 1.7% 84.3 4.2% ± ± ± ± ± ± 0.3–0.4 6.7 0.5% 7.5 0.4% 18.2 0.6% 46.2 1.3% 77.2 2.7% 86.8 6.9% ± ± ± ± ± ± 0.4–0.6 7.2 0.4% 8.4 0.3 17.8 0.5% 49.2 1.0% 77.0 2.1% 85.8 5.1% ± ± ± ± ± ±
158 0.6–1.0 7.2 0.4% 8.4 0.3% 17.8 0.5% 49.2 1.0% 77.0 2.1% 85.8 5.1% ± ± ± ± ± ± 1.0–2.0 6.7 0.5% 7.9 0.3% 17.4 0.5% 47.8 1.1% 76.2 2.2% 84.1 5.6% ± ± ± ± ± ± 2.0–100 6.8 0.7% 7.1 0.6% 14.2 0.7% 37.2 1.6% 65.4 3.4% 83.1 9.9% ± ± ± ± ± ±
Note. — Breakdown of AGN selection efficiency by log[L /1010L ] and E(B V ). Efficiencies were determined AGN ⊙ − using Monte Carlo to generate model SEDs with varying contributions from the three normal galaxy templates, add AGN components to the model SED, and introduce photometric errors. See 3.1.1 for further details. § Table 3.2. IR AGN Selection Efficiency mR (Vega) R/R200 fCl,spec σf
a3128 14.33 0.11 0.669 0.016 15.76 0.10 0.682 0.042 16.32 0.07 1.020 0.065 16.89 0.06 0.532 0.057 a3125 14.60 0.17 0.000 0.000 15.92 0.22 0.000 0.000 16.29 0.05 1.000 0.000 16.77 0.16 0.000 0.000 a2104 17.28 0.05 0.719 0.015 18.32 0.07 0.249 0.007 18.88 0.11 0.222 0.008 19.31 0.11 0.068 0.003 a1689 17.02 0.03 0.626 0.014 18.46 0.08 0.409 0.014 19.07 0.09 0.217 0.015 19.65 0.12 0.411 0.033 ms1008 19.10 0.08 0.693 0.054 19.79 0.04 0.293 0.013 20.35 0.08 0.225 0.014 20.91 0.11 0.084 0.010 ac114 19.00 0.10 0.744 0.069 19.84 0.06 0.925 0.072 20.37 0.13 0.399 0.050 20.83 0.14 0.913 0.353
Note. — A sample of spectroscopic completeness measurements as described in 3.3.1. The complete table is available from the electronic edition of the journal. A § brief sample is shown here for guidance regarding form and content.
Table 3.3. Spectroscopic Completeness
159 R/R200 fν(8 m)[Jy] C8µm fν(24 m)[Jy] C24µm (1) (2) (3) (4) (5) (6)
−4 +0.15 −4 +0.15 A3128 0.06 2.65 10 0.00−0.00 1.70 10 0.00−0.00 × −4 +0.18 × −4 +0.15 0.06 8.44 10 0.51−0.17 5.41 10 0.00−0.00 × −3 +0.14 × −3 +0.16 0.06 2.69 10 0.55−0.20 1.64 10 0.31−0.17 × −3 +0.14 × −3 +0.00 0.06 8.57 10 0.55−0.20 5.44 10 1.00−0.19 × −2 +0.14 × −2 +0.00 0.06 2.66 10 0.55−0.20 1.74 10 1.00−0.19 × −2 +0.14 × −2 +0.00 0.06 8.48 10 0.55−0.20 5.51 10 1.00−0.19 × −1 +0.14 × −1 +0.00 0.06 2.73 10 0.55−0.20 1.64 10 1.00−0.19 × −1 +0.14 × −1 +0.00 0.06 8.67 10 0.55−0.20 5.49 10 1.00−0.19 × −4 +0.50 × −4 +0.50 A3125 0.05 2.53 10 0.00−0.00 1.58 10 0.00−0.00 × −4 +0.00 × −4 +0.50 160 0.05 7.95 10 1.00 5.09 10 0.00 × −0.50 × −0.00 0.05 2.40 10−3 1.00+0.00 1.57 10−3 0.00+0.50 × −0.50 × −0.00 0.05 7.89 10−3 1.00+0.00 5.03 10−3 0.58+0.08 × −0.50 × −0.08 0.05 2.50 10−2 1.00+0.00 1.60 10−2 1.00+0.00 × −0.50 × −0.50 0.05 7.84 10−2 1.00+0.00 4.93 10−2 1.00+0.00 × −0.50 × −0.50 0.05 2.56 10−1 1.00+0.00 1.57 10−1 1.00+0.00 × −0.50 × −0.50 0.05 8.01 10−1 1.00+0.00 4.87 10−1 1.00+0.00 × −0.50 × −0.50 A2104 0.06 3.43 10−5 0.00+0.06 2.25 10−5 0.00+0.06 × −0.00 × −0.00 0.15 1.13 10−4 0.11+0.09 6.91 10−5 0.03+0.07 × −0.06 × −0.03 0.24 3.47 10−4 0.51+0.17 2.27 10−4 0.00+0.17 × −0.22 × −0.00 0.06 3.55 10−3 1.00+0.00 2.21 10−3 0.68+0.09 × −0.03 × −0.10 (continued) Table 3.4. MIR Completeness Table 3.4—Continued
R/R200 fν(8 m)[Jy] C8µm fν(24 m)[Jy] C24µm (1) (2) (3) (4) (5) (6)
−2 +0.04 −3 +0.09 0.15 1.10 10 0.93−0.07 6.65 10 0.73−0.09 × −2 +0.11 × −2 +0.14 0.24 3.63 10 0.70−0.23 2.16 10 0.80−0.21 × −5 +0.00 × −5 +0.06 A1689 0.06 2.31 10 1.00−0.09 1.42 10 0.00−0.00 × −5 +0.00 × −5 +0.06 0.06 7.49 10 1.00−0.09 4.56 10 0.00−0.00 × −4 +0.00 × −4 +0.08 0.06 2.33 10 1.00−0.09 1.43 10 0.04−0.03 × −4 +0.00 × −4 +0.09 0.06 7.17 10 1.00−0.09 4.45 10 0.71−0.13 × −3 +0.00 × −3 +0.09 0.06 2.23 10 1.00−0.09 1.45 10 0.81−0.11 × −3 +0.00 × −3 +0.09 0.06 7.10 10 1.00−0.09 4.32 10 0.81−0.11
161 × × 0.06 2.35 10−2 1.00+0.00 1.43 10−2 0.81+0.09 × −0.09 × −0.11 0.06 7.38 10−2 1.00+0.00 4.54 10−2 0.81+0.09 × −0.09 × −0.11 MS1008 0.07 2.53 10−6 0.00+0.11 0.00 100 1.00+1.00 × −0.00 × − −1.00 0.07 7.55 10−6 0.00+0.11 4.66 10−6 0.00+0.11 × −0.00 × −0.00 0.07 2.37 10−5 0.10+0.17 1.43 10−5 0.00+0.11 × −0.05 × −0.00 0.07 7.69 10−5 1.00+0.00 4.63 10−5 0.00+0.11 × −0.09 × −0.00 0.07 2.41 10−4 1.00+0.00 1.52 10−4 0.00+0.11 × −0.09 × −0.00 0.07 7.67 10−4 1.00+0.00 4.54 10−4 0.00+0.11 × −0.09 × −0.00 0.07 2.46 10−3 1.00+0.00 1.43 10−3 0.00+0.11 × −0.09 × −0.00 0.07 7.48 10−3 1.00+0.00 4.42 10−3 0.00+0.11 × −0.09 × −0.00 0.07 2.24 10−2 1.00+0.00 1.48 10−2 0.00+0.11 × −0.09 × −0.00 AC114 0.06 2.43 10−6 0.00+0.07 0.00 100 1.00+1.00 × −0.00 × − −1.00 (continued) Table 3.4—Continued
R/R200 fν(8 m)[Jy] C8µm fν (24 m)[Jy] C24µm (1) (2) (3) (4) (5) (6)
−6 +0.07 −6 +0.07 0.06 7.10 10 0.00−0.00 4.48 10 0.00−0.00 × −5 +0.08 × −5 +0.07 0.06 2.16 10 0.89−0.11 1.35 10 0.00−0.00 × −5 +0.00 × −5 +0.08 0.06 7.16 10 1.00−0.09 4.33 10 0.04−0.03 × −4 +0.00 × −4 +0.11 0.06 2.16 10 1.00−0.09 1.37 10 0.56−0.11 × −4 +0.00 × −4 +0.11 0.06 7.05 10 1.00−0.09 4.43 10 0.56−0.11 × −3 +0.00 × −3 +0.11 0.06 2.20 10 1.00−0.09 1.40 10 0.56−0.11 × −3 +0.00 × −3 +0.11 0.06 6.79 10 1.00−0.09 4.28 10 0.56−0.11 × −2 +0.00 × −2 +0.11 162 0.06 2.14 10 1.00 1.36 10 0.56 × −0.09 × −0.11
Note. — (1) The cluster name. (2) The median radius, scaled to the virial radius of the cluster, of galaxies that go into the bin. Columns (3) and (5) give the median observed frame fluxes in the 8 m and 24 m channels, respectively, of the model SEDs that make up each bin. Fluxes are calculated by integrating model SEDs with random combinations of the A10 star forming templates across the published instrument response functions. Because the A10 templates are not constructed to have identical SFRs in the 8 m and 24 m channels, this sometimes means that an SED within the target range in one channel will not appear in another. When a flux
bin is occupied in one channel and not in another, the empty channel has fν = 0 and C = 1. Columns (4) and (6) give the MIR completeness (C ) as defined in 3.3.2. λ − λ § Chapter 4
AGN in Low-Redshift Clusters
In this chapter, I will discuss the host galaxies of AGN ( 4.3) and their § distribution within their parent clusters ( 4.5). Together, these properties can help § to probe the fueling mechanisms important among AGN with low to moderate luminosities. It can also potentially probe the reason for the lower frequency of luminous AGN in clusters compared to the field. First, I will introduce the samples of AGN selected by the X ray and IR methods ( 3.1.1). Then I will examine the § differences between the two AGN samples and discuss why they have such limited overlap.
4.1. AGN Sample
In 3.1.1, I introduced two methods for AGN selection: the X ray and the IR § techniques. In this section, I will introduce the samples of AGN selected by the two techniques. The methods have different systematic uncertainties, so taken together, they will return a nearly complete sample of AGN. However, both techniques are likely to miss the low m ˙ accretion characteristic of radio AGN.
163 4.1.1. X-ray AGN Sample
In 3.1.1, I introduced my method to distinguish between X ray point sources § with genuine AGN contributions and those whose emission is likely due only to normal galaxy processes. This requires that I predict LX for the host galaxies of X ray point sources and determine if the luminosities measured by M06 are consistent with these predictions. The X ray luminosities reported by M06 are shown in Figure 4.1, where I compare them with the luminosities predicted for their host galaxies. The figure also shows the estimated systematic uncertainties on these luminosities, which are associated with the choice of energy correction factor (ECF) required to convert X ray count rates to fluxes. Many of the reported point sources require an AGN component, but several of the M06 point sources have very massive host galaxies, so their observed fluxes may arise entirely from host galaxy processes. Table 4.1 lists both X ray and IR AGN, their luminosities, and the basic parameters of their host galaxies.
The systematic flux error estimates in Figure 4.1 indicate that many X ray AGN have photon energy distributions that are poorly matched to theΓ=1.7 power law assumed by M06. Three such AGN are close to the boundary separating probable AGN from more ambiguous cases and have too large a soft X ray flux compared to their hard X ray flux to be consistent with a Γ = 1.7 power law. M06 did not correct for X ray absorption, and in the cases where the ratio of soft to hard X ray photons is too low for a Γ = 1.7 power law, absorption may explain the apparent discrepancy. However, objects whose soft X ray fluxes are unexpectedly large compared to the total cannot result from absorption.
Many narrow line Seyfert 1 galaxies (NLS1) show excess soft X ray emission (Arnaud et al. 1985). However, only one X ray source identified by M06 is a NLS1
164 (their Abell 644 #1), so the soft X ray excess common to NLS1s cannot explain the presence of excess soft X ray emission in 13 X ray sources with AGN like luminosities. Alternative explanations include soft X rays arising from gas that is photoionized by an obscured AGN (e.g. Ghosh et al. 2007), poor signal to noise in the X ray, and abnormally luminous thermal emission from hot gas. The ECF used to convert soft X ray photons to incident fluxes for kT = 0.7 keV thermal bremsstrahlung (assumed by Sun et al. 2007) is larger than the ECF foraΓ=1.7 power law by approximately 10%. This implies that two of the three suspect X ray AGN have luminosities sufficiently close to the threshold that they may reasonably be mis classified galaxies. This yields a possible contamination in the X ray AGN sample of approximately 10%, which is comparable to the estimated contamination of the IR AGN sample (see below).
In comparison to my sample of 23 X ray AGN from a parent sample of 35 X ray point sources with complete photometry, M06 found that 35 of their 40 point sources had X ray luminosities consistent with AGN. The larger fraction of
AGN reported by M06 may be attributed to their use of LX–LB relations, which show larger scatter than the K band relations. My method also introduces some uncertainty by estimating LK from the model SEDs, but this uncertainty is small ( 10%) compared to the scatter in the L –L relation. An additional difference is ∼ X K that M06 considered the two luminosity components separately and did not compare their sum to the measured luminosities. This was done subsequently by Sivakoff et al. (2008) and Arnold et al. (2009) in their studies of AGN in low redshift groups and clusters of galaxies. Their analyses are much closer to my method, and their samples included some of the clusters in the present sample (Abell 3128, 3125 and 644).
165 4.1.2. IR AGN Sample
In 3.1.1, I introduced a method to identify AGN based on the observed shapes § of their SEDs. This technique requires a threshold in likelihood ratio (ρmax), below which objects are considered AGN. I find 29 IR AGN using a selection boundary at the 99.8% confidence interval of the merged ρ distribution, less than 1 of which is expected to be a false positive.
With this selection threshold, 5 of 7 objects (71%) identified as AGN from the IRAC color color diagram (Stern wedge; Figure 4.2; Stern et al. 2005). The IR selection method also identifies 8 of the 23 X ray AGN (35%). The galaxies in the Stern wedge that are not selected from their SED fits fall just inside the boundary of the wedge, so they may be normal galaxies shifted into the wedge by photometric errors. Gorjian et al. (2008) find that 35% of X ray sources in the Bo¨otes field of the NOAO Deep Wide Field Survey (f > 8 10−15 erg s−1 cm−2) with detections in X × all 4 IRAC bands fall outside the Stern wedge, and Figure 14 of A10 shows that a substantial fraction of the point source (luminous) AGN in their sample fall outside the wedge as well. Given the high luminosities in both of these samples, it is not surprising that most of the lower luminosity AGN common in galaxy clusters fall outside the Stern wedge.
4.2. X-ray Sensitivity
With the LX–Lbol relation provided by Eqn. 3.7, I can determine whether the X ray non detection of many IR AGN results from some intrinsic difference between the two classes of AGN or if it is merely a result of the sensitivity of the X ray images used by M06. Eqn. 3.7 predicts that 9 (5) IR AGN with no X ray detections
166 should be more than a factor of 3 (5) brighter than the faintest point source in their parent clusters (M06). The M04 BCs produce still more X ray flux at fixed Lbol and yield 13 (12) IR AGN with significant X ray non detections with the same flux limits. The lack of detectable X rays from many IR AGN is consequently easier to explain if Eqn. 3.7 provides more reliable LX/Lbol than M04 for AGN in the cluster sample. The minimum detected flux in a given cluster may not always be a fair representation of the sensitivity for a given IR AGN due to variations in the Chandra effective area with off axis angle. However, the magnitudes by which many IR AGN in AC 114 exceed the minimum detected flux, sometimes more than a factor of
5, suggest that these AGN should have been detected if they obeyed the LX–Lbol relation of Eqn. 3.7.
The non detection of many IR AGN in X rays is qualitatively consistent with the results of Hickox et al. (2009), whose IR AGN selection relied upon the Stern wedge, and who found many strong IR AGN that could not be identified in X rays. At least some of the “missing” IR AGN could be highly obscured. An intervening
22 −2 absorber with NH = 10 cm would reduce the observed 0.5 2 keV flux by a factor 3, which is sufficient to explain many of the missing IR AGN. The missing AGN could also result from the large scatter about the mean Lν (2500A)–˚ αox relation. The AGN with the most significant X ray non detection exceeds the minimum reported flux by a factor of 7, which can be explained by α 0.4. Vignali et al. (2003) ox ≈ report a large intrinsic scatter about their best fit relation, and the combination of this scatter with in situ absorption could mask moderately luminous AGN from detection in X rays.
Finally, at least one IR AGN (A1689 #109) appears to be absent from the M06 sample due to X ray variability rather than as a result of absorption, intrinsic X ray faintness, or shallow Chandra imaging. This object is moderately
167 luminous (L = 2.1 1010L ), AGN dominated (f = 0.95), falls firmly in the bol × ⊙ AGN middle of the Stern wedge, and is very robustly detected from its likelihood ratio (ρ =4 10−77). Nevertheless, M06 find no X ray point source associated with this × object. In a more recent observation (Chandra Obs ID 6930, PI G. Garmire), A1689 #109 is associated with an X ray point source far brighter than the X ray sources reported by M06. It therefore seems likely that the IR AGN that require the most extreme values of αox could be accounted for by variability rather than factors like absorption or systematic X ray weakness.
4.3. Host Galaxies
I determine stellar masses and SFRs for AGN host galaxies after subtracting the AGN component from the SED. This introduces some additional uncertainty in the resulting masses and SFRs beyond the original photometric uncertainties, as discussed in Section 3.1.3. The uncertainty in the AGN contribution to the measured MIR fluxes can prevent detection of low level star formation in IR AGN. The SFR distribution among IR AGN is therefore biased toward high SFR.
Figure 4.3 shows the results of comparing galaxies hosting different types of AGN to one another and also to cluster galaxies as a whole. The stellar mass and SFR distributions of galaxies hosting X ray and IR AGN show no measurable differences with the distributions of all cluster members. Merging the X ray and IR AGN samples likewise yields no measurable difference. However, the hosts of IR AGN have high specific SFRs (sSFR) compared to the hosts of X ray AGN and to all cluster members at 98% and 97% confidence, respectively. The difference between the sSFRs of X ray AGN hosts and the full galaxy sample is not significant. However, X ray AGN hosts appear to have lower sSFRs than the average galaxy in
168 Figure 4.3, which is consistent with previous results using field galaxies (Hickox et al. 2009). The effect of non detections on the measured distributions may be important, even qualitatively. Many IR AGN hosts have upper limits on SFR that are smaller than the SFRs of the X ray AGN host galaxies with the lowest measurable SFRs. Therefore, if the IR AGN hosts had a distribution of SFRs similar to the X ray AGN hosts with measurable star formation, star formation would have been detected in most IR AGN hosts. This indicates that the IR AGN hosts without detected SFR cannot account for the higher sSFRs among IR AGN hosts with measurable SFR.
The IRAC color color diagram in Figure 4.2 probes of the nature of AGN host galaxies independent of their model SEDs by identifying the dominant source of their MIR emission (Donley et al. 2008). The MIR colors of X ray and IR AGN with the AGN components included are compared to all cluster members in Figure 4.2. Galaxies hosting AGN have unremarkable [5.8] [8.0] colors but do not extend − as far to the red as normal galaxies, which indicates that AGN are seldom found in starbursts or luminous infrared galaxies (Donley et al. 2008). AGN hosts also show much redder [3.6] [4.5] colors than typical for a red sequence galaxy, which − indicates a contribution of hot dust to the 4.5 m continuum. The colors of AGN hosts, especially IR AGN hosts, are influenced by the AGN continuum, but tests using the AGN and spiral galaxy templates indicate that only galaxies in the Stern wedge have more than 50% of their IRAC fluxes contributed by the AGN component. A two dimensional K S test confirms that, after excluding objects in the Stern wedge, the IRAC colors of both X ray and IR AGN still differ from normal galaxies at > 99.9% confidence.
Figure 4.2 also shows that X ray AGN are notably absent among the most vigorously star forming galaxies (those with the reddest [5.8] [8.0] colors) is − consistent with earlier indications that X ray AGN avoid the blue cloud in visible
169 color magnitude diagrams (CMDs; Schawinski et al. 2009; Hickox et al. 2009). Alternatively, the higher SFRs in these galaxies may obscure an AGN signature due to the high Ldust associated with star formation. The distribution of X ray AGN in Figure 4.2 is consistent with the results of Gorjian et al. (2008), who found that 16.8 0.3% of X ray–identified AGN outside the Stern wedge had very red ± [5.8] [8.0] colors consistent with vigorous, on going star formation. This population − comprises 20% of the X ray AGN.
The visible CMD provides a means to estimate the nature of galaxies in the absence of measurable star formation in the MIR. Figure 4.4 shows the CMD for each cluster after the AGN component has been subtracted from the IR AGN. The fraction of cluster members hosting an X ray AGN peaks on the red sequence, and the probability that the X ray AGN hosts are drawn from the parent cluster population is less than 10−3. This contrasts with AGN hosts in the field, where the X ray AGN fraction typically peaks in the green valley (Hickox et al. 2009; Schawinski et al. 2009; Silverman et al. 2009, henceforth S09) for AGN identified using either X ray luminosity or emission line diagnostics. IR AGN hosts, both in my sample of cluster AGN and in the field sample of Hickox et al. (2009), conspicuously avoid the red sequence. Like the difference between X ray AGN hosts and the parent cluster population, this result is significant at > 99.9% confidence. This indicates that the IR AGN sample has at most limited contamination by MIR excess early type galaxies of the sort studied by, e.g. Brand et al. (2009). Galaxies hosting IR AGN in clusters also show an important difference compared to their counterparts in the field. While only 1.5% of field galaxies hosting the IR AGN studied by Hickox et al. (2009) had 0.1(u g) colors redder than the median of the red sequence, more − than 20% of IR AGN in clusters have visible colors redder than the red sequence in their parent clusters.
170 I examined the SDSS g r colors of very red galaxies (V R) > 0.8 − − rest−frame in Abell 1689, which has the largest number of such objects, and found that most also appear red in SDSS colors. The most notable exception is Abell 1689 #192, which is an IR AGN. Therefore, its red color may only become apparent after the AGN contribution is subtracted. The qualitative agreement between the colors of very red galaxies in Figure 4.4 and their g r colors from SDSS suggests that − these objects are genuinely unusual and not the result of photometric errors. These galaxies also show substantial reddening of the AGN template in their A10 fit results, with E(B V ) = 0.4 and a trend for higher E(B V ) in galaxies with redder − − colors at 97% confidence. These results suggest that the unusually red galaxies in Figure 4.4 experience significant internal extinction that is not present in most galaxies.
Since the AGN component of the SED fit may account not only for a true AGN contribution but also for intrinsic variations about the normal galaxy templates, some or all of these very red AGN, which represent approximately 1/3 of the IR AGN sample, may not be true AGN. However, fewer than half (7/17) of objects with (V R) > 0.8 are identified as IR AGN; this implies that IR AGN − rest−frame must differ from normal galaxies not only in the visible but also in the MIR, and MIR fluxes are practically immune to extinction. Therefore, most of the IR AGN identified in this region of color magnitude space cannot be false positives selected due to their unusual visible colors but must have a genuine hot dust component in the MIR.
171 4.4. Accretion Rates
I use the bolometric luminosities of both X ray and IR AGN to measure the growth of their black holes and compare the black hole growth to the assembly of stellar mass in their host galaxies. The accretion rate of a black hole can be written as,
L M˙ = bol (4.1) BH ǫc2
where Lbol is the bolometric luminosity and ǫ is the efficiency of conversion between the rest mass energy (Mc˙ 2) of the accreted material and the energy radiated by the black hole. I assume ǫ =0.1, appropriate for a thin accretion disk around an SMBH with moderate spin (Thorne 1974) and determine Lbol as described in Section 3.1.2.
The accretion rates derived from Eqn. 4.1 for the X ray and IR AGN samples are shown in Figure 4.5. The left panel suggests that X ray and IR AGN have similar accretion rates, and a K S test reveals no significant difference between the two samples. This is surprising, since a na¨ıve explanation for the difference between X ray and IR AGN might be that the X ray and IR selection techniques depend differently on luminosity. Instead, the right panel of Figure 4.5 shows that the X ray and IR AGN samples have M˙ /SFR =3 10−3 and M˙ /SFR = 2 10−3, BH × BH × respectively. These ratios are comparable to the mean MBH /Mbulge in the local universe (2 10−3, Marconi & Hunt 2003), which indicates that the SMBHs in × cluster AGN are accreting at approximately the rate required to maintain the z =0 MBH –Mbulge relation. However, this is likely an artifact of the SFR detection thresholds, as the accretion rates of these objects are not large enough to produce outliers on the MBH –Mbulge relation in a Hubble time.
172 Figure 4.6 compares black hole accretion rates with host mass and sSFR. M˙ BH shows no significant correlation with sSFR, nor does it correlate with stellar mass among the X ray AGN sample. However, M˙ BH correlates with stellar mass among IR AGN at 99.5% confidence, weakening to 98% confidence among the merged AGN sample. This correlation may be related to the ability of more massive cluster members to retain more cold gas.
Figure 4.7 shows the relationship between black hole growth and stellar mass assembly in AGN host galaxies. The correlation of M˙ BH with SFR is extraordinarily strong (> 99.9% confidence), and both X ray and IR AGN appear to follow the same relation, with SFR M˙ 0.46±0.06. Netzer (2009) studied emission line AGN from ∝ BH SDSS and also found a tight correlation between SFR and AGN luminosity across nearly 5 dex in L . However, their SFR–M˙ relation (SFR M˙ 0.8 ) is steeper bol BH ∝ BH than mine at 5.7σ. Furthermore, Lutz et al. (2010) performed a stacking analysis of X ray identified AGN at z 1 and found no measurable correlation of SFR with ∼ 44 −1 Lbol for AGN with L2−10keV < 10 erg s . However, the millimeter bright, optically luminous QSOs studied by Lutz et al. (2008) appear to be consistent with both Netzer (2009) and Lutz et al. (2010). The qualitative similarity of Netzer (2009) and Lutz et al. (2010) to the results from the cluster sample suggests that these are manifestations of the same underlying relationship. That both X ray, IR and emission line selected AGN appear to show the same general trend toward higher
M˙ BH in hosts with higher SFR suggests that accretion rates in all of these objects are set by the size of the global cold gas reservoir. Such a relationship is also predicted theoretically as a result of large scale dynamical instabilities, which drive cold gas to the centers of galaxies where it can be accreted (Kawakatu & Wada 2008; Hopkins & Quataert 2010). However, the quantitative discrepancies between the
173 various observational signatures of star formation and gas accretion indicate that further work on the relationship between these phenomena is needed.
Figure 4.7 also compares star formation and black hole growth among the AGN sample with the median ratio found by S09 and the ratio needed to maintain the z =0 MBH –Mbulge relation. Some AGN show M˙ BH /SFR more than a dex below the ratio reported by S09 for field galaxies at z 0.8 and more than 0.3 dex below the ≈ rate needed to maintain the local MBH –Mbulge relation. However, if I include AGN hosts with no measurable star formation, the apparent discrepancy in M˙ BH /M˙ ∗ between the cluster AGN I measure and the field AGN of S09 becomes far less pronounced. The upper limits in Figure 4.7 fill in much of the empty space between the S09 median relation and the cluster AGN with measurable star formation, but the fraction of galaxies with M˙ /SFR < 2 10−3 is larger in Figure 4.7 than in BH × Figure 13 of S09 (7/39 versus 9/67). This difference grows (7/27) for the subset of
−2 −1 AGN with M˙ BH < 10 M⊙ yr , which is below the luminosity limit of the S09 sample. However, even the difference between the low luminosity subsample and the S09 result is not statistically significant (90% confidence). S09 project the evolution in the median SFR of their AGN sample to z = 0 and find that it agrees with the
SFRs measured in Type 2 AGN with log(L[OIII]) > 40.5 in SDSS. The median z = 0.2 SFR for the S09 AGN hosts is SF R 0.5 M yr−1, which is comparable ≈ ⊙ to the detection threshold in the cluster sample. As a result, the AGN measured in Figure 4.7 are more comparable to a high SFR subsample of the S09 AGN. However, there is no significant difference in the M˙ BH /SFR of high SFR versus low SFR AGN in S09. I therefore conclude that the ratio of M˙ BH to SFR in my sample of low z cluster AGN is consistent with the ratios observed in high z AGN in the field.
174 4.5. Radial Distributions
42 −1 Martini et al. (2007) found that luminous (LX > 10 erg s ) X ray AGN were more centrally concentrated in R/R200 than normal cluster members at 97% confidence. After pruning the AGN sample of suspect redshifts and applying improved K corrections, I assemble the radial distributions of the X ray and IR AGN samples in Figure 4.8. Figures 4.8a and 4.8b, which consider the X ray and IR AGN samples, respectively, have slightly different distributions of parent galaxies. This is because Spitzer pointings cover only the fields around X ray sources identified by M06 and not the full Chandra field of view. The IR AGN are selected from the cluster member catalog after SED fitting has been performed, so the radial distribution of IR AGN is guaranteed to be unbiased with respect to the sample of cluster galaxies, while X ray AGN must be compared to the distribution of all galaxies within the Chandra footprint. These different selection footprints lead to the different radial distributions shown in the solid red and black lines in Figure 4.8b. The difference is not significant, however, and has no impact on my conclusions.
In 3.1, I determined that the host galaxy of the X ray point source identified as § the cluster AGN AC114 5 by M06 had an erroneous spectroscopic redshift reported in the literature. The best fit model SED indicates that this source is a background AGN at z 0.99. Without this object, which is located at a projected distance phot ≈ R/R 0.2 from the center of AC 114, the significance of the difference between 200 ≈ the luminous X ray AGN and control samples drops to 89% confidence with a luminosity selected control sample and 92% confidence with a mass selected control sample. Consistent with the results of Martini et al. (2007), I also find no significant difference between the radial distribution of the full X ray AGN sample compared to the distribution of cluster members as a whole.
175 Following Martini et al. (2009), I also try a redshift dependent luminosity threshold M = M ∗ (0)+1 z in place of a fixed luminosity cut. The galaxy R,cut R − and AGN samples selected using this criterion show no significant differences in their R/R200 distributions. Martini et al. (2009) chose this evolving threshold to select a sample of passively evolving galaxies at fixed stellar mass. A mass threshold (M > 3 1010M ) appropriate for an elliptical galaxy at z = 0 with M = M ∗ × ⊙ R R,cut again yields no measurable difference between the radial distributions of X ray AGN and all cluster members. I conclude that the radial distributions of both X ray and IR AGN in galaxy clusters are consistent with the distribution of cluster members, although the agreement between cluster members and IR AGN is much better than between cluster members and X ray AGN.
4.6. Discussion
X ray selection of AGN is widely considered to be among the most robust means of selecting AGN (e.g. Ueda et al. 2003, S09, A10), because the measured hard X ray
24 −2 luminosity of a given AGN is largely insensitive to absorption if NH < 10 cm .
24 −2 Furthermore, the fraction of Compton thick AGN (NH > 10 cm ) is small, with 10% or less of all cosmic black hole growth taking place in Compton thick systems (Treister et al. 2009). Alternatively, AGN can also be robustly identified from their UV continuum emission after it has been absorbed by dust and re emitted in the MIR. If these techniques are similarly immune to the effects of absorption, they should yield very similar AGN samples. Instead, only 15% of AGN in galaxy clusters are identified by both X ray and MIR techniques.
Furthermore, it is clear that this dichotomy does not result solely from the relative luminosities of X ray and IR AGN. The IR AGN sample contains 5 9 objects
176 that should have been detected in X rays if they had αox similar to AGN identified using both selection methods. The most prominent of these is Abell 1689 #109, which has L 8 1045 erg s−1 but was not detected in the Chandra image used bol ≈ × by M06 to identify X ray AGN in Abell 1689. This AGN appears quite prominently in a subsequent Chandra image, indicating that its initial non detection was most likely the result of X ray variability. This example demonstrates that the absence of detectable X ray emission from an AGN candidate, even a very strong one, does not necessarily preclude the presence of an AGN. However, Abell 1689 #109 is not typical. The IR AGN with significant X ray non detections are not necessarily the most luminous. Instead, they reside in the clusters with the deepest X ray images. Indeed, all of the X ray non detections in AC 114 that fall within the Chandra image footprint are predicted to be at least 3 times brighter than the faintest reported X ray point source. Therefore, these non detections might indicate contamination of the IR AGN sample by one or more of the effects discussed in Section 3.1.1, e.g. intrinsic variation in the SEDs of normal galaxies or dust heating by AGB carbon stars. More observational and theoretical work on the dust emission in old stellar populations are required before the potential of these sources of MIR emission to mimic an AGN like SED can be quantified.
In the absence of detailed, calibrated models for “contamination” of MIR emission by old stars, I assume that this component is negligible. This implies that X ray selection alone can miss a large fraction of moderate to low luminosity AGN. This could have important implications for studies of star formation in clusters using MIR luminosities (e.g. Saintonge et al. 2008; Bai et al. 2009; Geach et al. 2009). This is especially important if authors assume that AGN can always be identified with X rays alone or that the MIR emission from galaxies with X ray excesses is always dominated by AGN emission. These assumptions imply that any MIR emission not
177 associated with an X ray AGN must be powered by star formation and that no MIR emission from a galaxy hosting an X ray AGN can be powered by star formation. The results indicate that these assumptions may lead authors to overestimate the number of cluster galaxies with vigorous star formation and to underestimate the number with moderate star formation. Therefore, more detailed tests for AGN than commonly applied are needed to correctly interpret the MIR luminosities of cluster galaxies. The implications of the resulting biases are explored in more detail in 5.7.1 5.7.2. §
A difference between X ray– and MIR–selected AGN samples also appears among field samples, which consist of more luminous AGN than the ones I study and use a different MIR selection method (Hickox et al. 2009). The color distributions of IR AGN selected using different techniques also differ from one another, but it is clear that galaxies hosting AGN identified from their X ray emission are dissimilar from galaxies hosting AGN identified in the MIR. Most notably, IR AGN hosts have significantly higher sSFRs than the average cluster galaxy, while there is no significant difference between the sSFRs of X ray AGN and the cluster population as a whole. Since SFR correlates well with cold gas mass, the higher sSFRs among IR AGN host galaxies suggests these galaxies have a larger fraction of their baryons in cold gas than X ray AGN hosts. However, the differences discussed in Section 4.3 are determined only for galaxies with measurable star formation. Several IR AGN are found in host galaxies that have both visible and IRAC colors consistent with passively evolving stellar systems.
The tight correlations between accretion rates of both X ray and IR AGN with SFR in their host galaxies suggests that the two classes are fueled by the same mechanism and are therefore fundamentally similar. Subject to the caveat described above, the larger sSFRs found in IR AGN hosts might explain the
178 apparent dichotomy of the two AGN classes despite their physical similarity. Larger gas fractions in IR AGN hosts could lead to larger average column densities in IR
AGN, depressing LX/Lbol in these systems. The presence of at least 5 of the 8 IR AGN with X ray counterparts on the red sequence, where there is little cold gas to participate in X ray absorption, tends to support this hypothesis (Figure 4.4). If cold gas fractions of AGN host galaxies influence the detectability of X ray AGN, this might also explain the dearth of X ray AGN in the green valley in clusters compared to the field. The X ray AGN in the present sample are weaker than the AGN usually studied in field galaxy samples, and a modest cold gas reservoir in green valley galaxies could more easily absorb enough X rays from an AGN with
41 −1 LX = 10 erg s to make it undetectable. Doing the same for an AGN with
43 −1 LX = 10 erg s , which is more typical for the field samples studied by, e.g. Hickox et al. (2009) and S09, would require a larger gas column.
Just over half (58%) of the M06 X ray point sources have detectable hard X ray emission, and therefore many AGN near the Chandra detection limits—those that preferentially lack hard X ray detections—could be hidden by a sufficiently large absorbing column. Of the 9 IR only AGN in AC114 whose bolometric luminosities imply that they should have been detected in X rays, only 3 would remain detectable
22 −2 in the soft X ray band behind a gas column with NH = 10 cm . This column density is large for Type I AGN, but it is not unusual for Type II AGN observed in X rays (Ueda et al. 2003). Furthermore, X ray and IR AGN seem to obey the same relationship between SFR and accretion rate where host SFRs are measurable. This is consistent with the hypothesis that the apparent dichotomy between X ray and IR AGN is false, and the shape of an AGN’s SED depends strongly on the amount of absorbing material between the observer and the central black hole.
179 The scenario I propose, in which absorption by cold gas in the host galaxy is responsible for the absence of detectable X ray emission from IR AGN, is consistent with the observed differences between the two AGN samples. However, verifying that absorption by the host ISM is indeed the cause of this observed difference will require deeper X ray observations to detect X ray counterparts and estimate absorption columns. If these data become available, spectral signatures of X ray absorption would confirm that absorption in the host galaxy hides some IR AGN from X ray detection.
180 Fig. 4.1.— Comparison of the X ray luminosities of X ray point sources from M06 (y axis) with the predicted X ray luminosities of their host galaxies (x axis). Points show the measured luminosities, and the “tails” connect each source to the luminosity estimated by separating its 0.5 8.0 keV X ray luminosity into soft and hard components using a Γ = 1.7 power law.− The length of the tail indicates how well the measured photon energies are described by a Γ = 1.7 power law, and consequently describes the systematic uncertainty on LX. Long tails belong to objects poorly described by a Γ = 1.7 power law. Heavy lines mark the line of equality (LX = Lhost), and the dashed lines show the 0.7 dex scatter about the empirical relations used to predict the X ray luminosity of± a given host galaxy. See Section 3.1.1 for the method used to predict X ray luminosities of normal galaxies.
181 Fig. 4.2.— Positions of both IR AGN (red triangles, upper panel) and X ray AGN (blue pentagons, lower panel) on the Stern et al. (2005) AGN selection diagram. The dashed, green trapezoid marks the Stern wedge. Filled symbols mark AGN whose colors were determined using only measured magnitudes; open symbols show colors determined using model SEDs to estimate magnitudes in missing IRAC bands. No model colors were constructed for normal galaxies. Passive galaxies are located in the lower left corner, normal star forming galaxies are found in the center, and (U)LIRGs occupy the upper right part of the galaxy sequence. The error bars in the upper left show the color uncertainty for a typical cluster member, including AGN.
182 Fig. 4.3.— Cumulative stellar mass, SFR and sSFR distributions of the X ray and IR AGN samples compared to the distributions for all cluster members. Neither of the AGN samples show any significant differences in either M∗ or SFR compared to the full sample of cluster members, nor does the merged AGN sample. However, IR AGN hosts have higher sSFR than both X ray AGN hosts and normal galaxies at 99% confidence, despite their similarities in M∗ and SFR.
183 Fig. 4.4.— Visible color magnitude diagrams showing the spectroscopically confirmed member galaxies (black squares), X ray AGN (blue hexagons) and IR AGN (red triangles) of each cluster. The contribution of the AGN component to the model SED has been subtracted from the IR AGN, leaving estimated host galaxy colors and luminosities. Typical uncertainties on colors and absolute magnitudes are 0.1 mag. See 4.3 for further discussion. §
184 Fig. 4.5.— Comparison of accretion rates (M˙ BH ; left panel) and SMBH growth rates relative to their host galaxies (M˙ BH /SFR; right panel). The distributions show different methods of estimating M˙ BH : directly from the SEDs of IR AGN (red dashed), applying ad hoc BCs derived from AGN with both X ray and IR identifications (solid black), and applying the M04 BCs (blue dotted). M04 BCs return significantly lower M˙ BH than the other two methods, which are consistent with one another. Short black and red arrows mark upper limits for X ray and IR AGN, respectively. The dashed vertical line marks the ratio required to maintain the z = 0 MBH –Mbulge relation (Marconi & Hunt 2003). See Section 4.4 for more on the various BCs.
185 Fig. 4.6.— Relationships of black hole accretion rates (M˙ BH ) to stellar masses and sSFRs. Black points and arrows show M˙ BH inferred from X ray luminosities using BCs from Eqn. 3.7, while red points and arrows mark M˙ BH inferred from integrating model SEDs. Stellar masses and SFRs are measured from SEDs and include the entire galaxy, not just the spheroidal component, as would be appropriate for the MBH –Mbulge relation.
186 Fig. 4.7.— Relationship between black hole growth and star formation in the present AGN sample compared to the AGN sample from COSMOS examined by S09. Green circles and arrows mark the S09 AGN. All other symbols are the same as in Figure 4.6. Lines mark the M˙ BH /SFR relation measured by S09 (solid) and the ratio required to produce the z =0 MBH –Mbulge relation (Marconi & Hunt 2003; dashed). Both the SFRs measured here and those reported by S09 are galaxy wide rather than bulge only SFRs.
187 Fig. 4.8.— Radial distributions of X ray AGN, IR AGN, and all cluster galaxies within 42 −1 their parent clusters. Panel a: luminous (LX > 10 erg s ; dot-dashed) and all X 10 ray AGN (dashed) compared to all cluster members with M∗ > Mcut =3 10 M⊙ 10 × (solid). Panel b: luminous (Lbol > 10 L⊙; dot-dashed red) and all IR AGN (dashed red) compared to cluster members with Spitzer coverage and M∗ > Mcut (solid red) and the unbiased sample of galaxies inside the Chandra footprint (solid black).
188 Name Martini Name Lbol LX M∗ SFR 43 −1 41 −1 10 −1 (10 erg s ) (10 erg s ) (10 M⊙) (M⊙ yr ) (1) (2) (3) (4) (5) (6)
a3128 004 a3128 9 1.5 0.5 3.8 3.2 0.9(stat) 1.9(syst) < 0.04 ± ± ± a3128 012 a3128 6 1.0 0.3 22.4 1.9 0.6(stat) 1.1(syst) < 0.35 ± ± ± a3128 092 a3128 2 — 22.4 1.1 0.2(stat) 0.6(syst) < 0.44 ± ± a3125 044 a3125 5 — 7.2 7.5 1.6(stat) 4.5(syst) 2.7 0.4 ± ± ± a644 011 a644 1 10.4 1.4 28.2 2.6 0.9(stat) 1.6(syst) < 1.38 ± ± ± a644 024 a644 2 — 4.5 7.6 1.8(stat) 4.5(syst) 1.2 0.4 ± ± ± a2104 024 a2104 4 — 7.2 2.0 0.6(stat) 1.2(syst) < 0.50 ± ± a2104 040 a2104 6 — 7.2 12.4 3.8(stat) 7.4(syst) 1.9 0.6 ± ± ± 189 a2104 046 a2104 5 — 36.3 1.7 0.5(stat) 1.0(syst) < 0.83 ± ± a2104 051 a2104 2 4.1 0.8 18.2 < 2.3 < 16300 ± a2104 063 — 1.9 0.9 — < 1.1 < 0.70 ± a2104 073 — 3.1 1.7 — < 0.2 < 7.78 ± a2104 075 a2104 1 171.1 16.1 643.1 < 8.1 < 16400 ± a1689 059 a1689 2 4.8 1.0 40.2 < 0.7 < 1.00 ± a1689 085 — 0.7 0.3 — < 0.6 < 282000 ± a1689 094 — 1.6 0.4 — < 0.2 3.5 1.1 ± ± a1689 106 — 1.6 0.5 — < 0.3 2.4 0.4 ± ± a1689 109 — 7.5 0.7 — < 0.1 < 24400 ± a1689 135 — 0.9 0.4 — < 0.0 < 0.25 ± (continued) Table 4.1. Identified Active Galactic Nuclei Table 4.1—Continued
Name Martini Name Lbol LX M∗ SFR 43 −1 41 −1 10 −1 (10 erg s ) (10 erg s ) (10 M⊙) (M⊙ yr ) (1) (2) (3) (4) (5) (6)
a1689 153 — 2.0 0.3 — 1.2 0.4(stat) 0.7(syst) < 3.35 ± ± ± a1689 177 — 2.4 0.5 — < 0.6 < 2.38 ± a1689 179 — 1.6 0.8 — < 0.4 < 0.58 ± a1689 186 a1689 1 — 26.5 26.6 7.4(stat) 8.7(syst) < 1.13 ± ± a1689 192 — 2.3 2.1 — < 1.1 < 1.10 ± a1689 217 — 8.7 3.0 — < 4.0 < 16.1 ± a2163 091 a2163 2 — 17.4 < 1.1 2.5 0.8
190 ± ms1008 005 ms1008 5 — 68.0 24.6 7.1(stat) 8.4(syst) < 2.18 ± ± ms1008 008 ms1008 3 9.7 3.2 120.1 < 7.0 < 1040000 ± ms1008 043 ms1008 4 — 17.8 7.1 2.1(stat) 2.6(syst) < 0.66 ± ± ms1008 048 — 18.2 4.9 — < 6.0 < 1110000 ± ms1008 096 ms1008 2 — 16.3 < 0.8 < 0.89 ac114 004 ac114 3 — 82.3 12.0 3.3(stat) 4.1(syst) 7.9 1.1 ± ± ± ac114 012 — 3.7 1.6 — < 1.9 < 0.62 ± ac114 041 — 6.8 3.0 — < 13.8 < 1.25 ± ac114 049 ac114 1 — 509.7 1.7 0.5(stat) 0.9(syst) < 0.34 ± ± ac114 053 — 4.5 2.0 — < 6.5 < 1.03 ± ac114 067 ac114 2 2.7 1.5 34.1 < 1.1 < 0.57 ± ac114 090 — 4.7 1.6 — < 5.8 < 1.28 ± (continued) Table 4.1—Continued
Name Martini Name Lbol LX M∗ SFR 43 −1 41 −1 10 −1 (10 erg s ) (10 erg s ) (10 M⊙) (M⊙ yr ) (1) (2) (3) (4) (5) (6)
ac114 106 ac114 4 — 41.9 < 0.7 3.9 0.6 ± ac114 120 — 8.0 1.7 — 5.0 1.6(stat) 3.0(syst) < 0.00 ± ± ± ac114 155 — 6.5 2.1 — < 0.8 5.7 1.0 ± ± ac114 159 — — — < 0.0 < 0.00 ac114 176 — 5.7 1.7 — < 4.7 < 2.02 ± 191
Note. — Brief sample table summarizing AGNs identified either by their X ray luminosity or their SED shapes. The full table is available from the electronic edition of the journal. (1) The name of this object in Table 2.3. (2) The name given to the X ray source by Martini et al. (2006). (3) The bolometric luminosity derived by integrating the direct component of the AGN contribution to the model SED. These luminosities are quoted only for IR AGNs. (4) Rest frame X ray luminosities in the 0.3 8 keV band from Table 4 of Martini et al. (2006). X ray luminosities are given only for X ray AGNs. (5) Stellar mass derived using the M/L coefficients appropriate for a solar metallicity galaxy with a scaled Salpeter IMF and applying the Bruzual & Charlot population synthesis model (Bell & de Jong 2001, Table 4). Systematic errors are derived by applying the M/L coefficients appropriate for a Salpeter IMF and the Pegase´ population synthesis model. Upper limits are given at 3σ of the statistical error only. (6) SFR derived either from the 8 m luminosity, the 24 m luminosity or by taking the geometric mean of the two, depending on the measurements available. Uncertainties include only statistical errors, and upper limits are quoted at 3σ in the more sensitive of the 8 m and 24 m bands. Chapter 5
Impact of Cluster Environment on Star Formation
In Chapter 4, I explored the relationships between AGN, their host galaxies and the cluster environment. Surprisingly, the AGN sample I examined shows no significant correlation between black hole growth and star formation, nor does it show any significant dependence on environment. This contrasts with the observed dependence of SFR on environment in clusters, which has been known since Osterbrock (1960).
In this chapter, I will explore the relationship between dust enshrouded star formation and environment. This includes a detailed subtraction of AGN contributions from individual galaxies, as described in Chapter 3. I employ a partial correlation analysis to distinguish the environmental dependence of star formation from its dependence on stellar mass ( 5.1). The results of the partial correlation § analysis provide guidance for my later analysis, which uses empirical completeness measurements to correct the observed cluster members to a statistically complete sample. I discuss the constraints these results can place on the mechanisms that drive the dependence of star formation on the cluster environment.
I apply the weights derived from the completeness corrections described in 3.3 § to the main cluster sample, which is a subset of the cluster galaxies in Table 3.1. These corrections allow an examination of the average environmental dependence
192 of M ( 5.2) and SFR ( 5.3 5.5). They also permit measurements of the redshift ∗ § § dependence of star formation ( 5.6). Before conducting these analyses, I perform § a partial correlation analysis to determine which observed properties of galaxies in clusters most strongly correlate with star formation ( 5.1). The results of the § partial correlation analysis inform which correlations receive further examination throughout the rest of the chapter.
5.1. Partial Correlation Results
The cluster environment can significantly alter the evolution of cluster member galaxies, as described in Chapter 1. However, attempts to distinguish between the physical properties that might cause these effects must confront a system of mutually correlated observables. For example, SFR depends on both projected local galaxy density (Σ10; Osterbrock 1960; Oemler 1974; Dressler 1980; Kauffmann et al.
2004) and position within the cluster (R/R200; Kodama & Bower 2001; Balogh et al. 2004; Christlein & Zabludoff 2005; Blanton & Berlind 2007; Hansen et al. 2009; von der Linden et al. 2010). Figure 5.1 demonstrates the correlations between SFR, position, projected galaxy density and M∗ among SFGs. It is not immediately clear which of these is the most fundamental.
Because M∗, R/R200 and Σ10 are all mutually correlated, it is difficult to tell which variable or variables drives the environmental dependence of star formation. Therefore, I use a partial correlation analysis to disentangle these dependencies. The mathematical formalism for partial correlation analysis is described in 3.2. Because § this analysis does not rely on completeness corrections, I include galaxies from all 8 clusters in the sample.
193 I consider only objects with measurements of all parameters under consideration and ignore galaxies with upper limits. This differs from the similar analysis conducted by Christlein & Zabludoff (2005), who also considered upper limits. As a result, my results are more sensitive than Christlein & Zabludoff (2005) to systematic effects like variations in sensitivity within or between clusters. Because of this, I do not rely directly on the strength of any partial correlations, but only on the presence or absence of such correlations. For variables with significant partial correlations, I perform stacking analyses, which can account for incompleteness. (See 5.2 5.6.) § §
I conduct a partial correlation study on a system of five variables: SFR, M∗,
R/R200, the Dressler & Shectman (1988) substructure parameter (δ), and projected
th local density out to the 10 nearest neighbor (Σ10). Σ10 includes the completeness corrections described in 3.3.3, which accounts for the presence of neighbor galaxies § that are not included in the sample. The partial correlation coefficients returned by the analysis are listed in Table 5.1. Table 5.1 shows that SFR depends strongly on R/R200 (rS,partial = +0.34), but it shows no significant dependence of SFR on
M∗ once the influence of R/R200 has been factored out (rS,partial = +0.09). This conflicts with earlier results, which generally find either that SFR depends only on
M∗ (Gr¨utzbauch et al. 2011; Rettura et al. 2011) or that SFR depends on both M∗ and environment (Christlein & Zabludoff 2005). One reason for this discrepancy is that Table 5.1 does not include non SFGs. The fraction of non SFGs is higher among more massive galaxies, which introduces a dependence of SF R on M . ∗ Another possible factor is the different range of stellar masses included in the sample. The samples of Gr¨utzbauch et al. (2011) and Rettura et al. (2011) extend down only to 3 1010M , while my sample extends to approximately 1010M . ∼ × ⊙ ⊙ If the cluster environment affects lower mass galaxies more strongly, my sample will be more sensitive to this effect. Because the efficiency of RPS should depend
194 on M∗, the expanded mass range improves the sensitivity of the present sample to mass dependent effects that distinguish between RPS and gas starvation.
Interestingly, SFR also shows no residual dependence on Σ10 (rS,partial = +0.02) once the Σ10–R/R200 and SFR–R/R200 correlations are taken into account. This suggests that the SFRs of cluster members are driven by the local conditions of the ICM rather than by interactions with nearby galaxies. I emphasize that this conclusion applies to SFR only. I make no attempt to test the relationship between morphology and Σ10 or R/R200. This result is also consistent with results from previous authors (Poggianti et al. 1999; Moran et al. 2006) who found that the processes that alter SFR and morphology are likely to be physically distinct. The determination that SFR is more closely related to R/R200 than to Σ10 distinguishes this result from those of Christlein & Zabludoff (2005), who do not discriminate between different environmental tracers. As a result, they are agnostic about the process(es) that drive the SF R –radius relation. My results are also distinguished from Christlein & Zabludoff (2005) because their partial correlation analysis, which accounts for upper limits, includes all cluster members rather than SFGs alone. This accounts for the lack of a strong anti correlation between M∗ and SFR, which is driven by a decline in the fraction of SFGs at higher M∗ rather than a reduction in the SFRs of individual SFGs.
SFR also shows no relationship to local substructure, as measured by δ, at fixed
R/R200. Indeed, even a two variable correlation test returns no correlation between δ and SFR (r = 0.03). This conflicts with Christlein & Zabludoff (2005), who S − reported a strong correlation of SFR with local substructure. However, δ requires a robust spectroscopic sample from which to measure local velocity dispersions. As a result, the substructure measurements for clusters with less complete spectroscopy are probably unreliable. I repeat the test with A3128 and A3125, which have
195 the most complete spectroscopy and are the only two clusters with significant substructure. The results are indistinguishable from the full cluster sample.
5.2. Mass–Radius Relation
In 5.1, I reported a strong correlation of SFR with R/R but no residual § 200 dependence of SFR on M∗ or of M∗ on radius. This might indicate that M∗ does not depend on R/R200, as indicated by von der Linden et al. (2010). However, it might also mean that the SFR–R/R200 correlation is strong enough to eclipse any other correlations. The galaxy sample examined in 5.1 includes only a few § hundred galaxies, and the sample preferentially excludes the most massive galaxies, which tend not to show active star formation. As a result, 5.1 might show no § correlation between M∗ and R/R200, even if the full cluster galaxy sample shows one. Christlein & Zabludoff (2005) found a strong partial correlation of mass with
R/R200. This correlation would be difficult to produce if BCGs are responsible for a false correlation of M∗ with R/R200, as von der Linden et al. (2010) claim, because normal cluster galaxies are much more numerous than BCGs. This conflict renders the
To test whether the present data support the existence of a radial trend in M∗,
I look for direct variations of M∗ with R/R200 without regard to correlations with other variables. In their examination of M∗ as a function of R/R200 in SDSS clusters, von der Linden et al. (2010) excluded BCGs from their galaxy sample on the basis of their unusual masses and radii (Hoessel et al. 1987; Oegerle & Hoessel 1991; von der Linden et al. 2007). They found no significant correlation of M∗ with R/R200 once they excluded BCGs. I first divide the galaxy sample into two samples with equal numbers of galaxies and apply a K S test to check for a difference between
196 their radial distributions. For this analysis, I use members of all 8 clusters, and I exclude BCGs as defined by von der Linden et al. (2007) from the sample. The results are shown in Figure 5.2a. The K S test returns a probability < 0.1% that the high and low mass samples have the same radial distributions, so massive galaxies are preferentially found closer to the centers of their parent clusters.
Figure 5.2 shows that M∗ depends on R/R200, even in the absence of BCGs. To probe the origin of this relationship, I weight members of the main cluster sample by their completeness and the average mass as a function of radius. The average mass in a given bin is,
N Σ wiM∗,i M = i=0 (5.1) ∗ N Σi=0 wi where N is the number of galaxies in the bin with M < 20. The w are the R − i weights derived from Eqn. 3.14. Figure 5.3 shows the resulting average masses as a function of radius. The two innermost radial bins show a strong excess compared to the best fit to the outer bins.
I construct a best fit power law to the six outer radial bins in Figure 5.3 to determine the strength of the mass excess in the first bin and to determine why my results differ from von der Linden et al. (2010). The best fit is shown by the solid line in Figure 5.3, which returns χ2 = 1.0. The mass in the innermost bin differs from the best fit model by 3.0σ, and the mass excess in the second radial bin is significant at 1.8σ. This indicates that the cluster core tends to host more massive galaxies than the outer regions, even in the absence of BCGs. Mass segregation among cluster galaxies can be introduced as the cluster relaxes to virial equilibrium, which occurs on the order of the cluster dynamical time. For a cluster at z = 0, the dynamical time is 2 Gyr, mass excess in the central radial bins could feasibly ∼ develop over times comparable to the ages of the clusters in the sample ( 7 Gyr). ∼ 197 Outside R =0.1R200, M∗ does not continue to fall toward larger radii. Instead, it shows a marginally significant increase. This can originate either from tidal stripping of galaxies near the cluster center or as a result of higher average SFR at larger radii. Galaxies closer to the center of the cluster when we observe them tend to spend more time near the cluster center, on average, than galaxies farther away. Therefore, they are subject to stronger tidal forces from the cluster potential and will lose more of their mass (Merritt 1983, 1984; Natarajan et al. 1998). While the higher SF R at larger R/R provides an alternative explanation for the increase in mass 200 with R/R200, it requires that galaxies form a significant fraction of their mass in the cluster environment. A typical cluster galaxy with sSFR =3 10−11 yr−1 will form × 20% of its stars between z = 1 and z =0.1, while the average mass varies by 75% ∼ between R =0.1R200 and R =0.4R200. Therefore, tidal stripping better explains the observed variation.
Finally, Figure 5.3 suggests a reason for the disagreement between my results and von der Linden et al. (2010). The signal comes primarily inside 0.05R200, which corresponds to 1.′5 at the median redshift of the von der Linden et al. (2010) ∼ sample (z 0.8). Due to SDSS fiber collisions, only a few galaxies inside 0.05R ≈ 200 will have redshifts in each cluster. This shifts the median of the innermost radial bin in the von der Linden et al. (2010) sample to 0.08R . This is comparable to the ∼ 200 second radial bin in Figure 5.3. If the innermost bin was absent from Figure 5.3, I would not identify any dependence of M∗ on R/R200, so the disagreement between von der Linden et al. (2010) and Figure 5.3 probably results from the SDSS fiber collisions.
198 5.3. Environmental Dependence of SFR
In Chapter 4, I examined the R/R200 distributions of AGN and found no significant difference between the positions of AGN and normal cluster members. The lack of radial dependence among AGN could be due to the small sample size, it could indicate a weak dependence of the amount of cold gas on R/R200, or it might mean that AGN fueling is poorly correlated with the total cold gas reservoir of its host galaxy. To test these hypotheses, I define a sample of galaxies with 8 m flux excesses as those galaxies whose measured 8 m flux exceeds the flux expected from a passively evolving galaxy matched in MK at more than 2σ. Figure 5.2b compares the radial distributions of galaxies with and without an 8 m excess. These objects include both SFGs and AGN, and the sample again excludes BCGs. The radial distribution of galaxies with 8 m excesses is indistinguishable from the full sample, but galaxies without an excess are located closer to the centers of their host clusters than the average cluster galaxy at 95% confidence.
The dependence of dust emission on R/R200 shown in Figure 5.2b is consistent with the established dependence of SFR on position within galaxy clusters (Kodama & Bower 2001; Balogh et al. 2004; Christlein & Zabludoff 2005; Hansen et al. 2009; von der Linden et al. 2010) and with our results in 5.1. One way to constrain the § origin of this effect, and by extension the SFR–density and SFR–radius relations, is to measure the average SFR as a function of radius. I weight individual SFGs by their total completeness (Eqn. 3.16) and bin them in radius to determine SF R : ΣNSF w SF R SF R = i=0 SF,i (5.2) Ngal Σj=0 wj where wSF,i and wj are the weights for SFGs and all galaxies, respectively. For this calculation, I define SFGs as all galaxies with SF R 3 M yr−1. This guarantees ≥ ⊙ that the results are not biased by variable sensitivities across the cluster fields.
199 I fit a power law to SF R as a function of R/R and find, 200
log SF R = (1.3 0.7) log R/R + (1.3 0.6) (5.3) 10 ± 10 200 ±
2 where SF R is the averaged in each radial bin; the fit yields χν = 1.2. This simple power law provides a good fit to the data. Despite the large statistical uncertainties on the best fit slope, Figure 5.4 clearly demonstrates higher SF R toward the outer regions of the stacked cluster sample. This mirrors the trend found in 5.1, which § is significant at >99.9% confidence. Interpretation of Eqn. 5.3 requires a model that accounts for projection effects, the distribution of orbits followed by cluster members, and the effect of different environmental processes on cluster members.
The RPS scenario makes at least one clear, qualitative prediction that I can use to evaluate its impact. Because RPS operates quickly compared to the cluster crossing time, the radial variation in SF R should be caused by variations in the fraction of SFGs (fSF ), and there should be little change in the SFRs of individual galaxies. The middle panel of Figure 5.4 shows fSF as a function of radius. There is a clear trend in f versus R/R , so the fraction of galaxies with SF R 3 M yr−1 SF 200 ≥ ⊙ declines toward the cluster center. However, the sample of SFGs also shows a lower sSFR among galaxies with R < 0.1 R200 relative to the average of the two ∼ outer bins. This difference is significant at 97% confidence, and it is most clear among the open points in the lower panel of Figure 5.4. This decline is seen in the aggregate rather than among individual SFGs, and it appears in both of the bins with R < 0.1R200, so galaxies in transition to passive evolution must make up a large ∼ fraction of the SFGs with R < 0.1R200. ∼ 200 5.4. TIR Luminosity Function
Another probe of the impact of environment on star formation is the TIR luminosity function (LF). The TIR LF is sensitive to the frequency of star formation in clusters and the rapidity with which it is quenched; this provides a strong empirical constraint on the types of processes that mediate the interaction between individual galaxies and the cluster environment. For example, Bai et al. (2009) found similar shapes (α and L∗) of the TIR LFs in the galaxy clusters that they measure compared to the field galaxy TIR LF. They argue that this similarity requires truncation of star formation on short timescales compared to the lifetime of star formation in individual galaxies. Such rapid transitions are inconsistent with processes like gas starvation and galaxy harassment.
To evaluate the conclusion that RPS dominates the evolution of star formation in galaxy cluster members, I examine the TIR LFs of the clusters in the main cluster sample. I construct the LF as described in 3.4.2, and the results appear in Figure § 5.5. The main cluster sample contains only 5 clusters, which prevents construction of subsamples that have different masses and similar redshifts. This prohibits reliable identification of effects that depend strongly on cluster mass.
The dashed vertical line in Figure 5.5 marks the expected LT IR of a galaxy with the A10 spiral SED and M = 20. This marks the approximate TIR completeness R − limit imposed by the requirement that M 20. I call this limit Lthresh. This R ≤ − T IR limit is representative only, and Figure 5.5 includes many cluster members that have M 20 and L < Lthresh. This is expected because cluster galaxies have lower R ≤ − T IR T IR sSFR than the field galaxies used to construct the A10 templates. In fact, 65% of galaxies with M < 20 and measurable (> 3σ) MIR emission are less luminous R − thresh thresh than LT IR . This means that the “true” LT IR is lower than the nominal value
201 thresh established from the spiral galaxy template. A prediction of the true LT IR , requires a model for the truncation of star formation in clusters, which is exactly what I want to measure. To be conservative, I restrict the fits to use only bins more luminous
thresh than LT IR . Above this limit, the weights given by Eqn. 3.16 will reliably correct to the full galaxy population.
In contrast to the finding of Bai et al. (2009), the individual clusters in Figure 5.5 show variations above the nominal completeness limit. For example,
10 A3128 has no galaxies with LT IR > 6 10 L⊙, while the other clusters in the ∼ × main sample show many galaxies in this range. This occurs despite the similar numbers of spectroscopic members in A3128 compared to A2104 and MS1008. In
thresh addition, the deficit of galaxies slightly more luminous than LT IR compared to the evolution corrected Coma LF varies significantly between clusters. One possible
thresh thresh reason for the deficit near LT IR is that the true LT IR appropriate for a sample of galaxies with M < 20 is more luminous than we estimate. Because sSFR is R − thresh lower in cluster members than in the field, it is unlikely that the true LT IR exceeds the nominal limit in Figure 5.5. The large fraction of IR detected cluster members with M < 20 and L < Lthresh (66%) supports this conclusion. Alternatively, R − T IR T IR the cluster environment might reduce SFRs in low LT IR cluster members more rapidly. This would explain why lower z clusters tend to have a larger fraction
thresh of SFGs with LT IR < LT IR : Low z clusters have more time to influence their members.
At least some of the variation between the IR LFs observed in different clusters may be caused by systematic uncertainties in the completeness corrections. Completeness corrections are only useful in regions of the clusters with both spectra and MIR photometry, so the possibility of azimuthal asymmetry is an important uncertainty. The LF of the stacked cluster sample averages over several selection
202 regions, so it is less subject to this uncertainty. The TIR LF of the stacked galaxy cluster sample is shown in Figure 5.6.
I fit Schechter functions to the LFs of both the Coma cluster and the stacked cluster shown in Figure 5.6. The Schechter function has the form, Φ∗ L α Φ(L)= e−L/L∗ (5.4) L∗ L∗ where Φ(L) gives the projected surface density of sources at TIR luminosity L, and α and L are the usual Schechter function parameters. I fixed α = 1.41 in the fit ∗ − to the cluster LF, which is the best fit value for the Coma LF (Bai et al. 2006). Le Floc’h et al. (2005) suggest that the faint end of the LF cannot evolve much with redshift for z < 1, so the faint end of the LF in the Coma cluster is likely to provide ∼ a good estimate of α in all galaxy clusters. The best fit to the stacked main sample has L = (6.6 1.1) 1010L . ∗ ± × ⊙
If clusters rapidly shut off star formation in galaxies that fall in from the field, as Bai et al. (2009) conclude, then the population of SFGs should include very few galaxies in transition from star forming to passive. As a result, the TIR LF of a cluster should have a similar shape to the field galaxy LF at its redshift. I therefore compare L∗ to the field galaxy LF at the median redshift of the stacked cluster sample, zmed = 0.24. Le Floc’h et al. (2005) found that the field galaxy LF evolves as L (1 + z)n, where n = 3.2+0.7. I employ this prescription to shift the Coma ∗ ∝ −0.6 cluster LF to zmed, and the result appears as the dashed line in Figure 5.6. The
′ evolution corrected Coma LF has L = 4.6+0.8 1010L , and it is apparent from ∗ −0.6 × ⊙ Figure 5.6 that it agrees well with the observed LF above L 4 1010 L . T IR ≈ × ⊙
Bai et al. (2009) found that the luminous ends of the TIR LFs of the Coma
∗ cluster and A3266 have similar shapes and that the LT IR for these clusters are indistinguishable from the field galaxy LF. The similarity between the stacked cluster
203 LF and the redshift Coma LF for L > 4 1010L agrees with Bai et al. (2009). T IR × ⊙ They argue that the similar LF shapes in clusters and in the field suggests that gas starvation is not a plausible mechanism to end star formation among cluster member galaxies. Because gas starvation operates slowly ( Gyr timescales), they conclude, ∼ it should produce many galaxies in the transition phase between SFGs and passive evolution. They found no such transition population. However, Figure 5.6 appears to show a deficit of galaxies with moderate SFRs (SF R 5 M yr−1) compared to ≈ ⊙ the expectation from the field LF. This could result from a transition population like the one implied by Figure 5.4. It is also possible that this deficit results from some selection effect not accounted for in our completeness estimates. The most obvious potential culprit is some residual dependence of spectroscopic member identification on color that is too weak to appear in a sample with the size of the one examined here.
As a test for radial gradients in the population of transition SFGs, I separate the galaxies in the main cluster sample into three radial bins with equal numbers of galaxies. The TIR LFs for the radial subsamples are shown in Figure 5.7. The
∗ LT IR increases slightly from the inner to the outer radial bin, and the maximum
LT IR in a given radial bin also increases monotonically toward large R. Intriguingly, the frequency of galaxies with M < 20 and L < Lthresh also increases with R − T IR T IR thresh radius at 2σ. Figure 3.12 indicates that galaxies immediately below LT IR should be detectable at all radii. While Figure 5.4 suggests that a population of transition galaxies is present, it is not clear to what extent uncorrected selection effects like those suggested above might contribute to the observed differences between radial bins.
204 5.5. Substructure and Preprocessing
Von der Linden et al. (2010) found a trend toward increased SF R at larger R/R200 that extended out to at least 2R200. They concluded that preprocessing in groups contributes significantly to the SFR–density relation. Galaxies in the cluster sample are restricted to R < 0.4R200, but A3128 shows significant substructure, so I compare it to the smooth clusters in the sample to estimate the impact of substructure SFRs among cluster galaxies. This allows an indirect test of the impact of group scale environments on SFGs, because coherent substructures in clusters should correspond to recently accreted groups.
Before I can compare the integrated SFRs in different clusters, I must first correct for the different numbers of galaxies in different clusters. The average sSFR naturally accounts for this variation, and it is therefore a better parameter to compare integrated SFRs between different clusters. I employ a method analogous to Eqn. 5.2 to calculate the sSFR and compare A3128, which shows significant substructure, to the other clusters in the main sample. I find sSFR = 9.0+1.1 10−12 yr−1 −1.2 × and sSFR =3.1+0.2 10−11 yr−1 in A3128 and in clusters without substructure, −0.2 × respectively. The evolution in the average SFR may have an impact on the observed difference, because A3128 is the lowest z cluster in the sample. I correct sSFR of A3128 to the mean redshift of the other clusters with SFR–z relation of Le Floc’h et al. (2005) and find sSFR =1.5+0.4 10−11 yr−1. −0.4 ×
Even after correcting for evolution in the cosmic SFR, clusters without significant substructure appear to have larger sSFR than A3128 at > 99.99% confidence. If the observed substructure is due to groups that have recently fallen into the cluster, the excess sSFR in clusters without substructure implies that the “average” group member is likely to have experienced preprocessing. This result
205 may be absent from the partial correlation results (Table 5.1) because only 12% ∼ of cluster members have ever been members of large groups (Berrier et al. 2009).
In Chapter 4 I discussed the quite different AGN samples returned by the IR and X ray AGN selection techniques. These segregated AGN populations might lead to biases in SFR indicators. Such biases would be especially important in studies that rely on X ray only AGN selection to remove contamination from their samples of SFGs. Samples whose redshift ranges exclude PAH emission from the IRAC bands, which renders identification of AGN like SEDs considerably more difficult, would be yet more vulnerable. To measure the importance of this bias, I compare the integrated star forming populations in my cluster sample with a full correction for AGN and with an X ray only AGN exclusion. If I relied exclusively on an X ray based AGN selection, the MIR luminosity contributed by unidentified AGN would lead to an overestimate in the integrated SFR of the cluster. In the case of A1689, I would overestimate the total SFR by 20%. Applied to all clusters simultaneously, this bias results in an inferred sSFR =7.2+1.5 10−11 yr−1 among −1.3 × the clusters without measurable substructure but no measurable change in A3128. Thus, uncorrected AGN contamination would dominate the observed difference in sSFR , and I would significantly over estimate the impact of preprocessing in the group environment.
5.6. MIR Butcher-Oemler Effect
The relative importance of gas starvation and RPS is also probed by the evolution in SF R as a function of cosmic time. The classic example of this is the Butcher Oemler effect (Butcher & Oemler 1978). Haines et al. (2009) constructed an analogous measurement with SFRs measured via νLν (24 m) among the LoCuSS
206 −1 cluster galaxies. They employed a SFR threshold of 8.6 M⊙ yr , and they found that f (1 + z)n with n = 5.7+2.1. Figure 5.8 shows their fit to f among the SF ∝ −1.8 SF LoCuSS clusters as a function of redshift. The fSF values for clusters in the present sample and for a higher redshift cluster sample measured by Saintonge et al. (2008) are superimposed. The 8 clusters in my sample, shown as the red squares in Figure 5.8 are clearly consistent with the Haines et al. (2009) result within the uncertainties.
However, the fit to the LoCuSS clusters systematically over predicts fSF in the
−1 Saintonge et al. (2008) clusters, despite the lower SFR threshold (5 M⊙ yr ) used by Saintonge et al. (2008).
In 5.5, I considered the impact of X ray only AGN identification on the § inferred sSFR . This becomes a more important consideration at high z, because the frequency of luminous AGN increases dramatically (Martini et al. 2009). Figure
5.8 includes two points for each cluster in our sample. One shows fSF with the IR
AGN selection included (filled triangles), and the other shows fSF that we would measure if we only knew about the X ray selected AGN (open triangles). The fSF inferred from the X ray only selection in AC114 differs by 1.6σ from the result when the full AGN sample is considered. This illustrates the contamination that X ray only AGN identification can introduce to integrated SFRs. This contamination becomes more severe, and appears in other clusters, for SFR thresholds less than the fairly high value employed by Haines et al. (2009).
5.7. Discussion
In 5.1 and 5.3 I examined correlations between environment, SFR and M . § § ∗ SFR shows a strong correlation with R/R200, and I found evidence for a transition population of low SFR galaxies. I interpret this population as evidence that galaxies
207 in clusters change gradually from star forming to passive. This interpretation was supported by a possible trend toward larger L∗ farther out in the cluster ( 5.4). T IR § I also found evidence ( 5.2) for tidal stripping of stellar mass from cluster members § and a rapid increase in the importance of RPS as galaxies approach 0.1R . ∼ 200
Here, I consider the results of 5.3 5.5 in the context of two competing § mechanisms to end star formation in cluster galaxies: RPS and gas starvation ( 5.7.1). I also examine the results of Haines et al. (2009), which are similar to our § measurements in 5.6, and consider how the Butcher Oemler Effect can probe the § end of star formation in cluster galaxies ( 5.7.2). §
5.7.1. Star Formation in Clusters
Thus far, I have presented several diagnostics for the impact of the cluster environment on star formation. These include partial correlation analysis, SF R and sSFR versus radius, and an examination of TIR LFs. One important result of this analysis is the absence of a correlation between SFR and Σ10 at fixed R/R200. This implies that interactions between individual galaxies have limited impact on star formation in cluster members, and I conclude that the SFRs of cluster members are controlled by hydrodynamic interactions between galaxies and the ICM.
There is disagreement in the literature concerning the importance of different mechanisms to shut down star formation in clusters. For example, Simard et al. (2009) determined that evolution in cluster SFRs is controlled by galaxy galaxy interactions because the growth in the fractions of early type and passive galaxies track one another very closely in their sample. This contrasts sharply with 5.1, § where the results suggest that interactions with the ICM are the dominant factor.
208 Of the hydrodynamic processes commonly considered (e.g. RPS and gas starvation), only RPS has been directly observed to work in nearby clusters (Kenney et al. 2004; Sivanandam et al. 2010). Therefore, the appropriate question to ask is whether the observations are consistent with RPS or if another mechanism is required to explain the observations.
Treu et al. (2003) determined that RPS works effectively for Milky Way like galaxies in a cluster with M = 8 1014M when R < 0.5R . The Milky Way vir × ⊙ 200 is fairly typical of SFGs in Figure 5.1, but the mass assumed by Treu et al. (2003) is about 2 times larger than the clusters in my sample. The sample of cluster galaxies is restricted to R < 0.4R200 in projection, so RPS should act efficiently on most galaxies in the sample. Nevertheless, the SFGs show none of the predicted signatures of RPS: Figure 5.4 shows a substantial population of SFGs in transition from star forming to passive, which might be supported by possible variations in L∗ with R/R ( 5.4; Bai et al. 2009). The partial correlation analysis reports T IR 200 § no residual correlation of SFR with M∗ at fixed R/R200, which is contrary to the prediction that RPS should affect low mass galaxies more strongly. In addition, the uniformity of the TIR LF between clusters, which Bai et al. (2009) interpreted as evidence for RPS, is weaker among the present sample.
To interpret Figures 5.4 and 5.7, projection effects and the influence of galaxy “backsplash” are critical. Projection effects will cause some galaxies at intrinsically large R/R200 to appear at small radii when the cluster is projected onto the plane of
−2 the sky. For an R density profile for galaxies and fSF vs. R/R200 as determined by von der Linden et al. (2010), only 30% of SFGs with projected R < 0.3R ∼ 200 actually fall within that region. This represents an upper limit, since the von der Linden et al. (2010) result also includes projection effects. Therefore, many SFGs at small projected R/R200 actually reside further out in the under dense parts of the
209 halo, where RPS contributes less. This suggests that SFGs that physically reside inside 0.1R200 have their SFRs reduced even further than implied by Figure 5.4. This may indicate that RPS starts to have a significant impact near this radius.
“Backsplash” refers to galaxies on nearly radial orbits that pass through the dense central region of the cluster and return to large R/R200. This effect can make radial gradients, such as the ones shown in Figure 5.4 particularly difficult to interpret because even galaxies presently at large radii may have passed near the cluster center in the past. Gill et al. (2005) report that 50% of galaxies in their simulations that have projected radii between 1 2R200 are backsplash galaxies, and
90% of these have been inside 0.5R200 at some point in the past. Pimbblet (2011) find that 60 6% of galaxies at R/R =0.3 are part of the backsplash population, ± 200 so they were even deeper into the dense central region of the cluster at some point in the past. A combination of projection and backsplash probably accounts for the absence of a clear feature of RPS in Figure 5.4.
One of the best ways to account for both projection and backsplash is to compare the observations to models that include these effects. This approach allows more reliable conclusions than simple, ad hoc arguments. Book & Benson (2010) developed a model for the stripping of galaxy halos by the ICM, which is the physical mechanism that drives gas starvation. Their “shocks” model predicts that galaxies under the exclusive influence of gas starvation should show (approximately) SF R (R/R )∼0.6 between 0.1 0.4R (their Figure 3). This is consistent with ∝ 200 200 our results in 5.3 ( SF R (R/R )1.3±0.7), but the large statistical uncertainty § ∝ 200 on the fit makes a more detailed comparison difficult. The normalization predicted by their model also agrees with our observations, again within large uncertainties. While the statistical uncertainties on our results preclude firm conclusions, the agreement between our observations and the Book & Benson (2010) model for gas
210 starvation suggests that gas starvation could explain the SFR–R/R200 relation. A larger cluster sample with complete coverage out to some fixed fraction of R200 is necessary to improve the observational constraints.
The large observational uncertainties in Figure 5.4 render the comparison between the observed radial dependence of SFR and the model of Book & Benson (2010) inconclusive. Given this uncertainty and the lack of other model predictions like the dependence of the TIR LF on R/R200, I will make a few qualitative arguments. Figures 5.4 and 5.6 demonstrate that a substantial fraction of cluster galaxies have lower LT IR than is typical for field galaxies of the same R band luminosity. Furthermore, Figure 5.7 indicates that the luminous end of the TIR LF may vary with R/R , and the bottom panel of Figure 5.4 shows that sSFR 200 among SFGs decreases toward the cluster center. This suggests that the cluster environment produces a reduction in both fSF and the characteristic SFR of SFGs. The strength of this reduction also varies between clusters (Figure 5.5).
In contrast to our results, Bai et al. (2009) found that the TIR LFs of many clusters are consistent with one another and with the field LF. They inferred that cluster galaxies only rarely occupy a transition phase between SFRs characteristic of field galaxies and complete passivity. From this, they determined that star formation in cluster galaxies must be truncated on short timescales compared to the lifetime of the cluster. Both my sample and Bai et al. (2006) show 1σ variations in the shape ∼ of the TIR LF in different R/R200 bins. However, I also find that the maximum observed LT IR increases monotonically from the innermost to the outermost radial bin, which suggests that the statistically insignificant change in L∗ may nevertheless be physical. These results hint that the higher density of the ICM near cluster centers reduces SFRs in individual SFGs before it eventually ends star formation. Projection effects and backsplash will both influence the observed trends. Projection
211 causes galaxies at large R/R200 to appear near the cluster center in the plane of the sky, while backsplash moves galaxies that had been processed near the cluster center back to the outskirts of the cluster. Both effects cause the projected trends to appear weaker than the true, three dimensional variations in the cluster. This suggests that the observed radial variation in sSFR of SFGs is real, and the trend with projected radius likely underestimates the intrinsic, three dimensional trend.
The LFs binned by R/R200 hint that SFGs near the cluster center have lower SFRs than SFGs further out. In combination with the dependence of sSFR on R/R200, this trend in the LFs suggests that a significant time elapses from the time when the cluster begins to transform a SFG and the end of star formation in that galaxy. Indeed, 66% of SFGs with M < 20 have L < Lthresh, where Lthresh R − T IR T IR T IR is the luminosity expected for a typical field spiral with M = 20. If 50% of R − thresh field SFGs with the same MR distribution had LT IR < LT IR , then 16% of cluster SFGs would be in transition. Combined with the gas consumption timescale of a typical spiral galaxy (2.4 Gyr; Bigiel et al. 2011), this implies a transition time of 400 Myr. This timescale is approximately twice the dynamical time of an ordinary ∼ spiral galaxy. This timescale favors RPS, which implies that galaxies should remain in a transition phase for approximately their dynamical time while the cold ISM is stripped. However, the assumption that 50% of field SFGs in an MR matched sample
thresh would have LT IR < LT IR is arbitrary. A comparison of the SFR–MR relations in clusters and in the field is required to measure the transition time more precisely.
If star formation in most cluster galaxies ends as a result of RPS, post starburst galaxies should be more frequent in clusters than in the field. This is a robust prediction of any scenario that results in a rapid transition of SFGs to passive evolution. Galaxies with K+A spectra, which are usually associated with post starburst populations, should remain visible for 100 Myr. This is short compared ∼ 212 to the cluster crossing time, so a large population of K+A galaxies relative to SFGs near the cluster center would be strong evidence that RPS plays an important role. Instead, Yan et al. (2009) report that galaxies with K+A spectra are less common in overdense environments like clusters than in the field at z 0.1. They suggest that ≈ K+A galaxies appear at constant absolute density, and that this density corresponds to the group scale at z 0. However, Dressler et al. (1999) instead found that the ≈ fraction of K+A galaxies is much higher in clusters than in the field, but von der
Linden et al. (2010) found no dependence of the ratio of NK+A/NSF on R/R200, so the observational evidence remains contradictory on this point.
Groups that have recently fallen into a cluster might appear as an excess in the substructure parameter (Dressler & Shectman 1988). In the present sample, only A3125 has a mass comparable to galaxy groups, and I have neglected that cluster in my analysis. Therefore, I cannot directly constrain the mechanism that drives SFR evolution in group members. However, sSFR is higher among clusters with no substructure than in A3128, which is the only member of the main sample with significant substructure. The lower sSFR in A3128 compared to the other clusters is consistent with the hypothesis that preprocessing in the group environment leads to lower SFRs among group members, which eventually fall into the cluster. However, a larger are more uniformly selected sample of clusters is required to draw firmer conclusions. In a recent study of SDSS galaxy clusters, von der Linden et al.
(2010) found a trend of SFR with radius that extended to 2R200. They concluded that preprocessing of galaxies in groups is likely to contribute significantly to the SFR–radius relation. The low energy density of the intragroup medium compared to the ICM renders RPS inefficient in groups. Instead, another mechanism that can operate in the appropriate density and velocity ranges, like gas starvation, is required.
213 5.7.2. Evolution
In 5.6 I suggested that the evolution of star formation in clusters can constrain § the mechanism(s) responsible for the appearance of the local SFR–density relation.
In particular, the rate of evolution of fSF probes the timescale for the cluster environment to shut down star formation in recently accreted field galaxies. Figure 5.8 shows the evolution in the fraction of SFGs measured in the MIR as a function of redshift. There is a clear trend for clusters a higher redshift to show larger SFG populations, as expected from the Butcher Oemler Effect, for example.
The measured evolution in fSF provides an estimate of the elapsed time required to bring about the end of star formation in cluster galaxies. Haines et al. (2009) report that f (1 + z)a, where a = 5.72.1. The measurements of Haines SF ∝ 1.8 et al. (2009) are consistent with the clusters in my sample, as shown in Figure 5.8. Approximately 70% of cluster member galaxies at z = 0 never had a massive companion before they entered the cluster environment (Berrier et al. 2009), so I assume that galaxies that fall into the cluster have the same LF as field galaxies. Le Floc’h et al. (2005) report that L∗ (1+ z)3.9 for field galaxies. If f among field T IR ∝ SF ∗ galaxies has the same redshift dependence as LT IR, the change in fSF as a function of redshift probes the timescale for the cluster environment to influence SFRs among cluster members. The ratio of fSF,clust to fSF,field has undergone approximately 1.25 e foldings since z = 1. The elapsed time over this redshift interval is 7.7 Gyr, so the e folding time for f /f is 6 3 Gyr. This timescale is longer than the SF,clust sf,field ± time for gas exhaustion in SFGs, which is approximately 2.4 Gyr (Bigiel et al. 2011).
The e folding time of fSF does not correspond directly to the truncation time for star formation in individual cluster members. New SFGs constantly fall into the cluster from the field, and this results in a longer timescale for fSF,clust/fsf,field
214 to decline. I have not accounted for this effect in the simple calculation above. However, the timescale for the end of star formation is long compared to the cluster dynamical time ( 2 Gyr). This suggests qualitatively that the process responsible ∼ for the end of star formation as galaxies fall into clusters must also operate on long timescales. Because the end of star formation in cluster members appears to extend over at least one cluster crossing, RPS is less likely to drive the observed fSF evolution. Because the Haines et al. (2009) best fit over predicts fSF among the high z clusters, it underestimates the timescale for the cluster environment to truncate star formation. A better estimate of this timescale will require a uniformly selected sample of SFGs across the entire redshift range of interest, and simulations of cluster mass assembly will be required to interpret the results more robustly than the qualitative analysis given here.
215 Fig. 5.1.— Correlations of star formation with position in the cluster (top row), projected local density (middle row), and stellar mass (bottom row). Galaxies with no measurable star formation are neglected. Colors denote the different clusters in the sample: A3128 (black), A3125 (red), A644 (blue), A2104 (green), A1689 (cyan), A2163 (magenta), MS1008 (orange), AC114 (violet). Large black points show the median values of the galaxy sample after it has been binned by SFR. SFR shows strong correlations with both R/R and Σ (r = +0.35 and r = 0.34, respectively), 200 10 S S − but no correlation with M∗. Partial correlation coefficients derived from these data are listed in Table 5.1.
216 Fig. 5.2.— Radial distributions of all cluster members scaled to R200. Panel (a) compares the radial distributions of low mass (blue dashed) and high mass (red solid) galaxies, divided into two equally sized subsamples at M = 3.9 1010 M . The cut × ⊙ two distributions differ at 99.9% confidence after we exclude BCGs as defined by von der Linden et al. (2007). Panel (b) compares the radial distributions of galaxies with (blue dotted) and without (red dashed) an 8 m flux excess to the distribution of all galaxies with 8 m detections (heavy black). The distribution of galaxies with no measurable excess shows a marginal difference compared to the distribution of all cluster members (95% confidence).
217 Fig. 5.3.— Mean stellar mass for galaxies in the stacked cluster sample as a function of radius. Brightest cluster galaxies are excluded from the fit because of their unusually large stellar masses and SFRs compared to other galaxies near the centers of clusters. The heavy line indicates the best fit power law to the 6 outer bins. The two innermost radial bins were excluded from the fit based on their large excesses. This resulted in 0.4±0.2 a reduction in total χ2 from 9.1 to 1.0. The best fit yields M R/R , and ∗ ∝ 200 the shaded region indicates the 68% confidence interval to the fit. The residuals are shown in the lower panel.
218 Fig. 5.4.— Dependence of star formation on R/R200 in the stacked cluster sample. −1 The SFG sample is restricted to galaxies with SFR > 3M⊙yr to avoid biases due to incompleteness. The top panel shows average SFR of all cluster members, and the 1.3±0.7 solid line indicates the best fit power law to the data SF R R/R . The ∝ 200 shaded region indicates the 68% confidence interval to the fit. The middle panel shows the fraction of SFGs for the same sample, with the best fit f [R/R ]0.4±0.2 SF ∝ 200 shown by the line and the 68% confidence interval shown by the shaded region. The bottom panel shows the averaged specific SFR (sSFR) among SFGs as a function of position. Filled squares show the radial bins used on the two upper panels, and open triangles show the same galaxies with wider bins. Galaxies with R/R200 < 0.1 have lower sSFR than galaxies outside 0.1R at 97% confidence. ∼ 200
219 Fig. 5.5.— Total infrared (TIR) luminosity functions for each of the 5 clusters in the main sample. Input galaxies are restricted to M < 20 and R < 0.4R . The R − 200 Coma cluster LF (triangles; Bai et al. 2009) is shown for reference. The solid black lines on each panel show a Schechter function fit to the Coma LF and adjusted to the redshift of the cluster with the prescription of Le Floc’h et al. (2005). This indicates the expected evolution of the field galaxies that enter the cluster. The dashed, blue lines mark the completeness limit imposed by the requirement M < 20. Some R − variation between clusters is apparent.
220 Fig. 5.6.— TIR luminosity function of the stacked cluster sample (filled squares) compared to the LFs derived by Bai et al. (2009) for Coma (filled triangles) and Abell 3266 (open triangles). The solid lines show the best fit LFs for the stacked sample (black) and for Coma (blue), and the shaded region shows the 68% confidence interval around the best fit to the stacked cluster LF. The dashed line shows the TIR LF of the Coma cluster, corrected to the median redshift of the stacked cluster sample with the prescription of Le Floc’h et al. (2005). The average LF determined by Bai et al. (2009) from two high z clusters (MS 1054 03 and RX J0152, red pentagons) is shown for comparison.
221 Fig. 5.7.— TIR LF divided into radial bins with equal numbers of galaxies. Each panel is labelled with the range of radii that contribute to the LF, where r = R/R200. The solid lines show the best fit Schechter function to the LF in each radial bin, and the shaded regions show the uncertainties on the fit. The fits are constructed from the points more luminous than the blue, dashed line, which shows the expected LT IR for the A10 spiral galaxy template. The red, dashed lines on each panel show the ∗ best fit LT IR, and its 1σ confidence interval is given by the shaded region. The three ∗ LFs hint at an increase in LT IR toward larger radii, but this trend is only significant at 1σ. ∼
222 −1 Fig. 5.8.— Integrated fractions of SFGs (fSF , SFR > 8.6M⊙yr ) in all 8 clusters as a function of redshift. Filled red triangles mark the clusters in our sample for which we successfully measure fSF , and red arrows mark the clusters for which we can produce only upper limits. Open red triangles mark the fSF that would be inferred from X ray only AGN identifications. The filled and open triangles overlap for all clusters except AC114. Open grey triangles indicate the LoCuSS clusters as reported by Haines et al. (2009), and open black pentagons mark the clusters measured by Saintonge et al. (2008). The solid line indicates the best fit fSF –z relation from Haines et al. (2009), and the shaded region shows the 1σ confidence interval for their +1.8 fit (f (1 + z)5.7−1.7 ). SF ∝
223 Partial rs Prob. (1) (2) (3) (4)
SFR M +0.091 5.15 10−01 ∗ × SFR R/R +0.342 2.11 10−05 200 × SFR δ 0.101 5.26 10−01 − × SFR Σ +0.018 8.40 10−01 × M R/R 0.087 5.14 10−01 ∗ 200 − × M δ 0.024 7.93 10−01 ∗ − × M Σ 0.012 8.90 10−01 ∗ − × R/R δ +0.188 2.77 10−02 200 × R/R Σ 0.576 1.96 10−17 200 − × δ Σ +0.068 5.41 10−01 ×
Note. — Partial correlation results for star forming galaxies derived from the Spearman correlation coefficients for the variables listed in columns (1) and (2). Column (3) gives the strength of the correlation between the two variables with the other parameters held fixed. Column (4) gives the probability that a correlation at least as strong as that observed might occur by chance among intrinsically uncorrelated data.
Table 5.1. Partial Correlation Results
224 Chapter 6
Conclusions & Outlook
I employed Spitzer observations of 8 low z galaxy clusters to construct 0.4 m to 24 m SEDs for cluster members. I fit these SEDs with the SED template library of A10, and I used these fits to identify AGN and to characterize cluster members.
6.1. AGN and Their Host Galaxies
One of the most important results from Chapter 4 is that the AGN identified by the IR method have very little overlap with AGN identified in X rays. I compared the host galaxies of AGN identified using the two methods and determined that, while their masses and SFRs are indistinguishable, IR AGN reside in galaxies with higher sSFRs than both X ray AGN hosts and the parent sample of cluster galaxies. The hosts of X ray AGN have sSFRs that are somewhat lower than but consistent with the sSFRs seen in cluster galaxies as a whole. The difference between X ray AGN hosts and normal cluster galaxies is significant only when comparing their positions in visible color magnitude and MIR color color diagrams. X ray AGN hosts are not found in the regions of these diagrams associated with vigorous star formation, while IR AGN sometimes are.
I also find that accretion rates of both X ray and IR AGN correlate strongly with SFR in host galaxies with measurable SFRs. This suggests that X ray and IR
225 AGN are physically similar and are fueled by the same mechanism. I hypothesize that the larger sSFRs seen in IR AGN hosts indicate larger cold gas fractions in these galaxies, and suggest that this could account for the apparent dichotomy between X ray and IR AGN. A moderately large cold gas column density of 1023 cm−2 could suppress the X ray emission from the IR AGN enough that we would be unable to detect them. The presence of IR AGN but not X ray AGN in galaxies with very red optical colors, indicative of strong absorption, lends credence to this hypothesis. It might also be verifiable directly by deep X ray observations of either AC 114 or Abell 1689 to search for X ray emission from IR AGN and to determine if such X ray emission shows evidence for absorption intrinsic to the host galaxy. For example, the most luminous IR AGN with no X ray counterpart in Abell 1689 could be detected by Chandra with S/N = 3 per resolution element at 4 keV —the energy cutoff for
23 −2 objects with NH = 10 cm —in 160 ks. This would allow a crude model spectrum to be constructed and the intrinsic absorption column to be measured. However, the long integration time required for this project makes it practically difficult.
Following Martini et al. (2007), I compared the radial distributions of AGN and all cluster members. I eliminated one AGN with a spectroscopic redshift from the literature that incorrectly identified a background quasar as a cluster member. Without this object, the significance of their result that luminous X ray AGN
42 −1 (LX > 10 erg s ) are more concentrated than cluster members as a whole is reduced to 90% confidence. While this result is no longer significant, it would ∼ be worthwhile to extend the present sample using archival Chandra imaging of additional clusters to either confirm or refute that X ray luminous AGN are more concentrated than the galaxy populations of their parent clusters. It is unlikely, however, that a similar exercise using IR AGN would yield a positive result, as the
226 radial distribution of IR AGN agrees very closely with the distribution of cluster galaxies.
6.2. Star Formation in Clusters
In Chapter 5, I discussed the dependence of SFR on environment in galaxy clusters and what it might tell us about how the cluster environment shuts down star formation among galaxies as they fall into the cluster. I employed multiple diagnostics, which included a partial correlation analysis, examination of SF R versus R/R200, and the TIR luminosity function.
The partial correlation analysis indicated that SFR among SFGs correlates most closely with R/R200 rather than Σ10. This suggests that hydrodynamic processes related to the density of the ISM are the dominant mechanism responsible for the end of star formation for R< 0.4R200. For the galaxies in these samples, any contribution of galaxy galaxy interactions like galaxy harassment are sub dominant and do not contribute enough to be detectable in the present sample.
To constrain which of the popular hydrodynamic processes (e.g. RPS and gas starvation) dominates, I measured the distribution of star formation within the cluster. I found that the variation of SF R is consistent with predictions from the gas starvation model of Book & Benson (2010), but the observational uncertainties are large. I also found evidence that the SFRs of individual SFGs vary with R/R200. Specifically, SFGs near the center of the cluster have slightly lower SFRs than their counterparts outside the cluster core. The frequency of such objects suggests that the transition timescale is > 400 Myr, which is intermediate between ∼ the timescales expected for RPS and gas starvation. In general, the presence of a
227 significant population of transition galaxies favors gas starvation over RPS. While the observations favor gas starvation, they remain somewhat ambiguous, and the literature is divided in favor of one process or the other. Future observations with a different strategy may help to resolve this question.
6.3. Future Work
In Chapter 5, I introduced the idea that the redshift dependent behavior of star formation can probe the mechanism primarily responsible for the end of star formation in cluster galaxies. I have recently started work on a sample of 5 high z clusters out to z = 0.83. I plan to analyze the entire sample in a uniform manner and take advantage of extensive MIPS and IRAC observations to obtain robust
AGN identifications and measure the evolution in fSF . This evolution is sensitive to ttrunc/taccrete, which should be fairly straightforward to predict from simulations of cluster formation. This technique can also be generalized to apply not only to clusters but to groups and to the field as well. This will allow a more general study of the timescale for the end of star formation as a function of environment and can potentially identify the process(es) responsible as a function of environment.
With a sufficiently large sample of groups and clusters, it is possible to measure the correlation of residuals about the fSF –z relation with various cluster parameters.
For example, a correlation of the residuals with TX would support my result from 5.1 that the ICM drives the transformation of galaxies from star forming to passive, § and the slope of the correlation would provide constraints on the specific mechanism.
Alternatively, a tight correlation of the residual fSF with Σ10 would indicate that galaxy galaxy interactions control star formation in individual clusters. This project
228 is well suited to the data set available in the NOAO Deep Wide Field Survey, which I will be working with while at NOAO.
In Chapter 3, I indicated that the IR AGN selection method should return only 1 false positive. However, astrophysical false positives, which would appear as ∼ AGN to the algorithm due to SED components that are not included in the models, could be an important factor. An additional avenue of research is to investigate possible sources for this contamination. One obvious source is circumstellar dust around thermally pulsating AGB stars. Such dust should be heated to 1000K ∼ Kelson & Holden (2010), and this could appear to be dust heated by an AGN instead. I plan to submit a Herschel proposal during OT 2 to facilitate a study of dust emission from TP AGB stars in post starburst galaxies. These data will be sensitive to any dust enshrouded star formation in these galaxies and will allow me to separate any measured Spitzer MIPS emission from K+A galaxies into components due to star formation and due to TP AGB stars.
Finally, the nature of the host galaxies with very red optical colors in Figure 4.4 remains a conundrum. I have obtained NIR spectra of several IR AGN in Abell 1689 with the LUCIFER imaging spectrograph on LBT. I plan to examine these spectra to check for high ionization emission lines that would unambiguously indicate the presence of an AGN. These spectra will also allow a test to determine whether any of these unusually red objects happen to be background galaxies. However, I consider this possibility to be remote, because the spectroscopic and photometric redshifts of these objects agree fairly well, and the photometry spans the peak of the stellar continuum.
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