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Full Text (Pdf) The Canada–France Deep Fields–Photometric Redshift Survey: An Investigation of Galaxy Evolution Using Photometric Redshifts by Mark Brodwin A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Astronomy and Astrophysics University of Toronto c Copyright by Mark Brodwin 2004 ° Abstract The Canada–France Deep Fields–Photometric Redshift Survey: An Investigation of Galaxy Evolution Using Photometric Redshifts Mark Brodwin Doctor of Philosophy Graduate Department of Astronomy and Astrophysics University of Toronto 2004 Progress in the study of galaxy evolution has traditionally followed from improvements in spectro- scopic measurement techniques and subsequent groundbreaking surveys. The advent of large format CCD detectors, coupled with the demonstrated success of the photometric redshift method, has given rise to a new, potentially very powerful alternative. It has, in fact, motivated the present detailed in- vestigation of the potential of photometric redshift surveys to complement, or in some cases, supersede traditional spectroscopic surveys in galaxy evolution studies. This Thesis describes a new deep, wide–field, multi–colour imaging survey, 10 times deeper and 30 times larger than its spectroscopic predecessor, the Canada–France Redshift Survey (CFRS). Highly accurate photometric redshifts, calibrated using hundreds of spectroscopic CFRS galaxies, were measured for tens of thousands of objects, with typical dispersions of only σ/(1+z) < 0.06 to Iab = 24 for z 1.3. ∼ ≤ A new Bayesian method to measure the galaxy redshift distribution is developed. The accuracy of the method, which incorporates the full redshift likelihood function of each galaxy in an iterative analysis, is demonstrated in extensive Monte Carlo simulations. Iab and Rab redshift distributions, along with the run of median redshifts, are measured in various magnitude ranges, with special attention given to quantifying both random and systematic errors. We measure the evolution of galaxy correlations with redshift, a primary observable of the structure formation process, correcting for the dilutive effect of photometric redshift errors on the clustering signal. The high z 3 correlation amplitude seen in this work provides compelling evidence for the biased galaxy ∼ formation paradigm. The measured galaxy correlations from 0 <z < 3 are in excellent agreement with ∼ ∼ the findings of the largest, state–of–the–art spectroscopic studies. For the 1– and 2–point statistics of the galaxy distribution studied in this Thesis, the measurement accuracy is limited not by the photometric redshift error, but rather by the effect of cosmic variance, whose contribution to the total error budget is dominant. Therefore, future studies will be well served by adopting the photometric redshift approach, the efficiency of which will enable them to survey the hundreds or thousands of square degrees required to obtain a fair sample of the Universe. This Thesis is dedicated to my grandmothers, Ethel Brodwin and the late Marjorie Dykhuis, and to my parents, Barry Brodwin, Susan Dykhuis and Murray Gold. Your love, support, encouragement, and unswerving faith in me made this work possible, and made me who I am today... Acknowledgements This work benefitted greatly from the contributions, both scientific and otherwise, of many people. I’d like to thank my supervisor, Simon Lilly, for his guidance, advice, and tremendous physical insight. His uncommon generosity with his time and resources, particularly as this Thesis was coming together, was invaluable. I would like to thank Ray Carlberg, Bob Abraham, Peter Martin and Bill Clarke for their advice and encouragement at some of the most dire moments over the last few years. I appreciate innumerable important discussions with faculty and postdocs at the University of Toronto, including Ray Carlberg, Bob Abraham, Barth Netterfield, Henk Hoekstra, Ue–Li Pen, Gary Felder, and at ETH Z¨urich, notably Cristiano Porciani, Peder Norberg and Kim–Vy Tran. I’d like to acknowledge my collaborator, Henry McCracken, for his expertise and dedication as we figured out how to reduce the unwieldy datasets produced by the first generation of mosaic cameras. I am grateful to David Crampton and David Schade for their assistance at the beginning of this project during a trip to HIA and CADC in Victoria, to Chris Pritchet for observing some of our CFH12k data, and to Chuck Steidel for providing us with his Lyman break galaxy catalogue prior to publication. I’d like to thank my external examiner, David Hogg, for his careful reading of my thesis, insightful comments, and at–ease demeanor during my defence. I have learned a tremendous amount about astronomy, computers and programming from the gradu- ate students, both current and former, at the department. In particular, I’d like to thank Allen Attard, Chuck Shepherd, Hy Trac, Mike Gladders, Tracy Webb, Kris Blindert, Rosemary McNaughton, Marcelo Ruetalo and many others for giving me the benefit of their experience. I couldn’t have gotten through this without the great friends I made in Toronto, the parties, the way- too-late-night conversations, the frequent sushi dinners, the hockey pools and playoff games, and the occasional sessions for music and introspection. Rosemary McNaughton, Allen Attard, Patrick Carey, Hy Trac, Marcelo Ruetalo, Laura Miglio, Mike Allen, and Arno Dirks come readily to mind. Scruffy the cat is grateful for being looked after by many of these people during my frequent trips. I’d also like to thank Kim–Vy Tran, Helmut Katzgraber, Xian and Jacques for their incredible hospitality on the many occasions I visited ETH, Coni Aurelio for making all the arrangements (always with a smile), and Marcella Corollo for making me feel so welcome. Marcella, Vy, and Laura are especially thanked for spoiling me with their culinary mastery! And finally, last on this page but first in my heart, I’d like to thank Cherry Rose Miranda for her unwavering support, sage advice, and incredible patience over the last 4 years. She’s seen me through all the ups and downs, endured the long absences, kept me focused, motivated and honest to myself, all while successfully making her own dreams come true. Cherry, you’re a real inspiration to me — I couldn’t have done it without you. To those I have not explicitly listed, I offer my apologies and my thanks. This work was funded by the Ontario Graduate Scholarship, the Walter C. Sumner Foundation, NSERC, ETH Z¨urich, and the University of Toronto. Contents List of Figures xi List of Tables xiii 1 Introduction 1 1.1 A Brief History of Galaxy Redshift Surveys . ....... 1 1.2 Weaknesses and Biases Inherent in Spectroscopic Redshift Surveys . 4 1.3 Validation of a New Method: Photometric Redshifts . ...... 5 1.4 ThisThesis ......................................... ... 6 2 Data Observations and Reduction 7 2.1 ObservationsLog..................................... .... 7 2.2 Preprocessing . ... 8 2.2.1 Bias, Overscan, and Dark Subtraction . 8 2.2.2 FlatFielding ..................................... 8 2.2.3 De–Fringing the CFH12k Images . 10 2.3 AstrometricCorrection.............................. ........ 11 2.4 Photometric Calibration . ........ 12 2.4.1 Z–bandCalibration ................................... 15 2.4.2 Galactic Extinction Correction . ...... 17 2.4.3 Conversion to AB Magnitudes . 17 2.5 CFDF Photometric Redshift Survey . 17 2.6 Masking of Saturated Stars and Residual CCD Defects . 18 2.7 CatalogueExtraction............................... ........ 19 2.8 CFDF–PRS Photometric Catalogue . ....... 21 2.8.1 MagnitudeErrors .................................. 21 3 Data Characterization and Verification 27 3.1 Comparison With CFRS Photometry . ....... 27 3.2 Colour–ColourPlots ................................ ....... 29 3.3 NumberCounts....................................... 29 3.4 Star/GalaxySeparation ............................... ...... 33 3.4.1 Morphological Component . 33 3.4.2 ColourComponent.................................. 36 3.4.3 Quantitative Voting Algorithm . ...... 39 3.4.4 External Constraint: Stellar Number Counts . ..... 41 3.4.5 Tests of the Stellar Classification Algorithm . ........ 42 4 Photometric Redshifts in the CFDF–PRS 47 4.1 TheAlgorithm ...................................... 47 4.2 Calibration Using CFRS Spectroscopic Redshifts . ....... 48 4.3 Photometric Redshift Accuracy for Iab > 22.5Galaxies ................... 50 4.4 Error Measures for Photometric Redshifts . ....... 51 4.5 Characterization of Catastrophic Errors . .......... 53 4.6 Photometric Redshift Accuracy for z 3 Lyman Break Galaxies . 55 ∼ 4.7 Characterizing the Redshift Likelihood Functions Via Confidence Intervals . 57 5 Method: Constructing Accurate Photometric Redshift Distributions 59 5.1 Bayesian Photometric Redshift Distributions . ........ 60 5.2 Direct summation of N(z)andIteration ............................ 60 5.3 Convergence Properties . 61 5.3.1 Formalism..................................... 61 5.3.2 Convergence in Simulations . 62 5.4 Test of the Method: Monte Carlo Simulation . ...... 63 5.4.1 SimulationResults ................................. 64 6 Photometric Redshift Distributions in the CFDF–PRS 69 6.1 ComparisonwiththeCFRS ............................. ...... 69 6.2 CFDF–PRS Iab 24 Redshift Distribution . 71 ≤ 6.3 Theoretical Expectations for the CFDF–PRS Redshift Distribution . ......... 71 6.4 Evolution of N(z) with Survey Depth . 72 6.5 Median Redshifts in the CFDF–PRS . 74 6.6 Comparison to COMBO–17 and HDF
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