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Tools for Cosmology - Combining Data from Photometric and Spectroscopic Surveys

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Tools for Cosmology - Combining Data from Photometric and Spectroscopic Surveys Tools for Cosmology - Combining Data from Photometric and Spectroscopic Surveys Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Sujeong Lee, B.S., M.S. Graduate Program in Physics The Ohio State University 2019 Dissertation Committee: Klaus Honscheid, Advisor Christopher M. Hirata Samir Mathur Richard J. Furnstahl c Copyright by Sujeong Lee 2019 Abstract Cosmic acceleration is the most surprising discovery in modern history. While the phenomenon has been proven by a plethora of cosmological observations, the under- lying mechanism is still a mystery. There have been various attempts to understand the driver of cosmic acceleration in a form of \dark energy" or \modified gravity", but none of these has compelling evidence. This thesis contains my PhD research projects dedicated to find the origin of cosmic acceleration. In Chapter 2, I describe the DES-CMASS (DMASS) galaxy sample constructed from images taken from the Dark Energy Survey (DES). The sample is designed for a joint analysis of the exist- ing BAO and RSD measurements from BOSS using the CMASS galaxy sample and a galaxy-galaxy lensing measurement from DES. We show that DMASS successfully replicates CMASS in many ways, such as by comparing galaxy bias, angular correla- tion functions, and redshifts. Chapter 3 describes the DES Y1 analysis for extended cosmological models focusing on modified gravity (MG), which I contributed. DES Y1 shear measurement significantly improves the existing MG constraints. We show that the resulting MG constraints are consistent with general relativity. In the latter part of the chapter, I forecast the detection of MG parameters for DMASS to demon- strate the capability of DMASS to achieve tighter constraints by cancelling galaxy bias. Finally, in Chapter 4, we study the information content of the three-dimensional galaxy correlation function and power spectrum when realistic scale cuts are applied. ii We find that two estimators are complementary to each other and combining the two yields small improvement for joint constraints. iii Acknowledgments To begin with, I would like to express my sincere gratitude to my advisor, Klaus Honcheid, for his continuous support of my Ph.D study and research. His consistent encouragement always motivated me to keep moving forward, which has shaped my career path as a scientist. Klaus has always been a caring, patient and was also very helpful and supportive during my postdoc job search. As I neared the end of my thesis, he offered great advice and support that helped me complete my thesis. I could not imagine having a better advisor and mentor for my Ph.D study. I also want to thank Chris Hirata for his insightful advice on my research. His deep understanding of physics helped me overcome theoretical challenges. In addition, I would like to thank the rest of my thesis committee, Samir Mathur and Richard J. Funstahl, for their encouragement, insightful comments, and probing questions. I am also grateful for my collaborators - Hee-Jong Seo, Ami Choi, Niall MacCrann, Michael Troxel, and Jack Elvin-Poole, who engaged in meaningful and inspiring dis- cussions. I am particularly in debt to Eric Huff and Ashley Ross, who have dedicated many hours to helping me not only with my research, but also in maintaining my mental health. I wish to thank my friends in Korea -Yoonkyung Hwang, Sugyeong Kim, Jeong- Ok Lee and Sula Lee- who have remained such good friends with me for 13 years, supporting me from the other side of the globe. My thanks also go to my friends at iv OSU, including Hui Kong, Ben Buckman, Paulo Montero, Bianca Davis, and Mathew Digman, who helped me with English and were good office mates. Finally, I could not have reached such a fulfilling life without the love and en- couragement of my family, who have been trusting and supporting of me spiritually throughout my life. v Vita 2011 . .B.S. Kyung Hee University, Korea 2013 . .M.S. Kyung Hee University, Korea Publications Research Publications \Producing a BOSS-CMASS sample with DES imaging" Lee, S., Huff, E. M., Ross, A., Choi, A., Hirata, C., Honscheid, K., MacCrann, N., Troxel, M. A., Cawthon, R., and Elvin-Poole, J. Accepted to Monthly Notices of the Royal Astronomical Society \Dark Energy Survey Year 1 Results: Constraints on Extended Cosmological Models from Galaxy Clustering and Weak Lensing" Abbot, T. M. C. et al. (DES Collaboration) Phys. Rev. D 99, 123505 Fields of Study Major Field: Physics vi Table of Contents Page Abstract....................................... ii Acknowledgments . iv Vita ......................................... vi ListofTables....................................x List of Figures . xi 1. Introduction..................................1 1.1 Overview ................................1 1.2 History: the discovery of accelerating universe . .3 1.3 Modern Cosmology: the Basic . .5 1.3.1 FLRW metric and Friedmann's equations . .5 1.3.2 Distances and Geometry . .8 1.3.3 Density Fluctuations and Structure Growth . 10 1.3.4 Correlation Function and Power Spectrum . 11 1.4 Dark Energy . 14 1.5 ModifiedGravity............................ 16 1.6 Cosmological Probes of Cosmic Acceleration . 20 1.6.1 TypeIaSupernovae ...................... 20 1.6.2 Cosmic Microwave Background Radiation . 24 1.6.3 Baryonic Acoustic Oscillation . 25 1.6.4 Redshift Space Distortion . 27 1.6.5 Weak Gravitational Lensing . 30 vii 2. Producing a BOSS-CMASS sample withDESimaging .............................. 33 2.1 Introduction .............................. 34 2.2 Data................................... 40 2.2.1 BOSS DR12 CMASS Sample . 40 2.2.2 DES Y1 Gold Catalog . 42 2.2.3 Differences between the SDSS and DES photometry . 46 2.3 Constructing the Model . 50 2.3.1 Overview of the Algorithm . 50 2.3.2 The Training and Validation Sets . 52 2.3.3 Obtaining True Distributions with the Extreme-Deconvolution Algorithm............................ 53 2.3.4 Application to the Target Galaxies . 54 2.3.5 Excluding Low Probability Galaxies . 59 2.4 Systematic Error Characterization . 61 2.5 Comparison with the BOSS CMASS Sample . 68 2.5.1 Number Density . 68 2.5.2 Angular Correlation Function . 70 2.5.3 Redshift Distribution . 78 2.5.4 Difference in Galaxy Bias . 83 2.6 Conclusion . 88 3. Dark Energy Survey Year 1 Results: Constraints on Extended Cosmological Models from Galaxy Clustering and Weak Lensing . 90 3.1 Introduction .............................. 91 3.2 Theory ................................. 93 3.3 Data................................... 94 3.3.1 DES data . 94 3.3.2 External data . 95 3.4 Analysis................................. 97 3.5 Results ................................. 98 3.6 Forecast for DMASS . 102 3.7 Conclusion . 107 4. Complementary Information in Fourier and Configuration Space for Cos- mological Observations . 108 4.1 Introduction .............................. 108 viii 4.2 Power spectrum and Correlation function . 111 4.2.1 Modeling estimator data using the streaming model . 112 4.3 Signal to Noise of estimators . 120 4.4 Cosmological Parameter Constraints . 129 4.4.1 Individual estimator projections . 129 4.4.2 Combining and Comparing Constraints . 131 4.5 Discussion and Conclusion . 138 5. Conclusion................................... 140 Appendices 143 A. The impact of redshift tails in BOSS CMASS on galaxy bias . 143 B. Differences between CMASS SGC and NGC . 149 C. Analytic way of marginalizing high k scales in band power fisher matrix withoutinverting ............................... 154 Bibliography .................................... 156 ix List of Tables Table Page 3.1 Parameters and priors used to describe the measured two-point func- tions, as adopted from Y1KP. Flat denotes a flat prior in the range given while Gauss(µ, σ) is a Gaussian prior with mean µ and width σ. 99 2 2 B.1 χ =dof of three probes calculated between two different samples. χsys=dof in the third column are calculated with systematic weights of DMASS obtained in Section 2.4. Values in the parentheses are corresponding PTE values. SGC and FULL in bold stand for CMASS in SGC and fullCMASS. ............................... 150 x List of Figures Figure Page 1.1 The Hubble diagram for the Pantheon sample. The top panel shows the distance modulus for each SN; the bottom panel shows residuals to the best fit cosmology. Distance modulus values are shown using G10 scatter model. Figure is from Scolnic et al. (2018). 23 2.1 The two-dimensional histograms of CMASS galaxies from Stripe 82 in the g r vs. r i color plane. The left panel shows SDSS, the right − − panel shows DES colors of the same galaxies. The colorbar shows the number of galaxies binned in each histogram bin. The red line is the d? cut. CMASS galaxies look bluer in the DES photometry and the d? cut discards almost half of the CMASS galaxies by crossing the most dense region. The grey contours in the right panel show the full distribution of DES Y1 GOLD galaxies in the color plane. The grey contours show that blindly lowering the d? cut results in accepting more non-CMASS galaxies. 45 2.2 The response functions for the griz SDSS (shaded) and DES (solid lines) filters as a function of wavelength (amstrong) with the spectral energy density (SED) distribution of an elliptical galaxy at z = 0:4 (black solid line). Near the 4000 amstrong break where the g r − transition happens, the SDSS r filter (shaded) covers slightly lower wavelength than the DES r filter (solid lines) does. This implies the same galaxy near z = 0:4 looks redder in the SDSS photometry than intheDESphotometry. ......................... 47 xi 2.3 Accuracy of model membership probabilities assigned to the test set. Galaxies in the test set are binned based on their probability assigned by the probabilistic model. The x-axis shows 20 bins of the assigned probability, and the y-axis shows the fraction of true CMASS galaxies in each bin. If the model successfully recovers the observed distribution of CMASS in the test region, the fraction of true CMASS galaxies in each bin should be identical to the assigned model probability.
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