UNIFORM CHEMICAL ABUNDANCES OF OPEN CLUSTERS USING THE CANNON by AMY E. RAY Bachelor of Science, 2013 Mississippi State University Mississippi State, MS Master of Science, 2017 Mississippi State University Mississippi State, MS Submitted to the Graduate Faculty of the College of Science and Engineering Texas Christian University in partial fulfillment of the requirements for the degree of Master of Science December 2018 ACKNOWLEDGEMENTS IwouldliketothankDr.Frinchaboyforbeinganexcellentadvisor. Thank you fellow graduate students, my committee, my parents, and my puppy Andromeda “Annie” Ray. I would like to acknowledge grant support for this research from the National Science Foundation (AST-1311835 & AST-1715662). Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the U.S. Department of Energy Office of Science. The SDSS-III web site is http://www.sdss3.org/. SDSS-III is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS-III Collaboration including the University of Arizona, the Brazil- ian Participation Group, Brookhaven National Laboratory, Carnegie Mellon University, University of Florida, the French Participation Group, the German Participation Group, Harvard University, the Instituto de Astrofisica de Canarias, the Michigan State/Notre Dame/JINA Participation Group, Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, Max Planck Institute for Extraterres- trial Physics, New Mexico State University, New York University, Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Span- ish Participation Group, University of Tokyo, University of Utah, Vanderbilt University, University of Virginia, University of Washington, and Yale University. Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High- Performance Computing at the University of Utah. The SDSS web site is www.sdss.org. SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, the French Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofisica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo, Lawrence Berke- ley National Laboratory, Leibniz Institut fur Astrophysik Potsdam (AIP), Max-Planck- Institut fur Astronomie (MPIA Heidelberg), Max- Planck-Institut fur Astrophysik (MPA Garching), Max-Planck- Institut fur Extraterrestrische Physik (MPE), National Astro- nomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatario Nacional/MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autonoma de Mexico, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, Univer- sity of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University. ii Contents ListofCommonAcronymsandTermsinAstronomy viii 1 Introduction 1 1.1 ChemicalAbundances(Metallicity) . 2 1.2 Open Clusters . 4 1.3 Goals . 5 2 Data Collection and Interpretation 9 2.1 VictorBlancoTelescope ........................... 9 2.1.1 TheHydraMulti-FiberSpectrograph . 11 2.1.2 Data Preparation . 11 2.1.3 Cluster Membership Determination from (Frinchaboy & Majewski 2008) . 13 2.2 SpectralAnalysis ............................... 15 2.2.1 Line Broadening Mechanisms . 16 2.2.2 Curve of Growth . 19 2.2.3 Boltzmann Equation . 20 2.2.4 Saha Equation . 21 2.3 Important Stellar Parameters . 22 2.3.1 E↵ective Temperature . 23 2.3.2 Surface Gravity . 23 2.3.3 Metallicity . 24 2.3.4 Stellar Parameters and The Cannon ................ 25 3 Results 27 3.1 The Cannon .................................. 27 3.1.1 Training Set . 28 3.1.2 IndividualStatResultsandDataQuality. 31 3.1.3 Verification of Cluster Membership and Bulk Cluster Metallities . 33 3.2 TotalClusterSample ............................. 33 4 Discussion 35 4.1 Summary . 35 4.1.1 Comparison to Other Surveys . 36 4.1.2 Discrepancies . 42 iii 4.1.3 NewValues .............................. 44 4.2 Future Work . 44 A The Cluster Sample 46 Vita Abstract iv List of Figures 1.1 An example of a star cluster over time. The young cluster in the leftmost panel has most of its stars on the main sequence. With time, more stars move o↵the main sequence, and from the turno↵point an age can be estimated. 5 1.2 Average iron abundances for a sample of open clusters. The plus symbols connected by red lines indicate measurements for the same cluster. This highlights the wide scatter in abundances across surveys that are discussed in Yong et al. (2012). 6 2.1 The Blanco 4 m telescope at CTIO in Chile. 10 2.2 An example of a Cassegrain telescope. The Blanco 4 m has a similar setup. 10 2.3 The Hydra fiber positioner system that used to be on the Blanco 4 m. 11 2.4 A histogram from Frinchaboy & Majewski (2008) that shows the radial velocity distribution of all stars with proper motion data that are in the fieldofthecalibrationclusterNGC2682. 13 2.5 These graphs show the steps in the membership analysis process for NGC 2682 that was done by Frinchaboy & Majewski (2008). (a) shows the result of the radial velocity distribution convolution with a Gaussian kernel to smooth the histogram shown in Figure 2.4, (b) shows the same smoothing process that was done for stars that fall outside of the cluster radius of NGC 2682, (c) shows the probability distribution for the cluster where non- members appear ouside of the main peak, and (d) shows a 1D Gaussian fit to the cluster distribution in (c). 14 2.6 CMD showing all of the stars within the cluster radius for NGC 2682. The larger black circles are members from both RV and proper motion deter- minations, crosses are stars that were found to be non-members, triangles are stars with high RV, but no proper motion values and the small grey circles are other non-member stars (Frinchaboy & Majewski 2008). 15 2.7 This is an example of curve of growth for the Sun. The dashed lines are separating the three di↵erent regions of the three functional forms (CarrollOstlie). 20 2.8 An example of how surface gravity impacts the strength of lines. The surface gravity is lowest at the top spectrum and highest at the bottom spectrum. As surface gravity increases, the pressure broadening wings increase. 24 v 3.1 One-to-one plots showing a comparison between the values obtained using The Cannon and the APOGEE DR14 labels for the training set. 32 4.1 A one-to-one comparison of [Fe/H] values for open clusters in common between Santos et al. (2009) and this study. The grey shaded area indicates the 0.17 scatter above and below the blue one-to-one line. 37 4.2 A one-to-one± comparison of average [Fe/H] values obtained by Reddy et al. (2013; 2015) and this study for the open clusters in common. 38 4.3 A comparison of [Fe/H] values for open clusters in common from Netopil et al. (2016) and this study.......................... 40 4.4 A comparison of average [Fe/H] values for in common open clusters from this survey and from the literature compilation in Table 4.4. Blue stars are [Fe/H] averages for open clusters IC 4756 and orange diamonds are [Fe/H]averagesforopenclusterNGC5822. 41 vi List of Tables 1.1 The open clusters with no prior chemical abundance measurements. 7 1.2 The open clusters with chemical abundance measurements that will be used to verify the values obtained in this study for the 12 open clusters listed in Table 1.1. 8 3.1 Values for Teff ,logg, and [Fe/H] from APOGEE compared to The Cannon output for the training set. 28 4.1 Average in common open cluster iron abundance from Santos et al. (2009) compared to ( this study). 36 4.2 Average in common open cluster iron abundance comparison between Reddy et al. (2013; 2015) and this study..................38 4.3 Average iron abundances for open clusters in common between Netopil et al. (2016) and this study.......................... 39 4.4 Average open cluster iron abundance for open clusters in common between this study and other literature studies. 41 4.5 Average open cluster iron abundance for clusters with no prior measure- ments. .................................... 44 A.1 The output from The Cannon for every star in the open cluster sample. The errors for Teff ,logg, and [Fe/H] are 141 K, 0.29 dex, and 0.17 dex, respectively. Cluster members used in± the average± Fe/H cluster± de- termination are indicated by a “Y” in the last column and stars that were either non-members or dwarf stars are indicated by “N”. 46 vii List of Common Acronyms and Terms in Astronomy Metals – All elements heavier than hydrogen and helium • Metallicity – The mass fraction of metals in a star with respect to • hydrogen – [Fe/H] – Often used as a proxy for metallicity in stellar spectroscopic studies – [Fe/H] = log(Fe/H)star log(Fe/H) − dex – Decimal exponent (deprecated) • – Unit used for abundance value (e.g. [Fe/H] = 0.06 dex) LTE – Local