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High-Redshift Astrophysics and Cosmology with Intensity Mapping HIGH-REDSHIFT ASTROPHYSICS AND COSMOLOGY WITH INTENSITY MAPPING by Patrick C. Breysse A dissertation submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy. Baltimore, Maryland March, 2017 ⃝c Patrick C. Breysse 2017 All rights reserved Abstract Upcoming intensity mapping surveys will provide a powerful probe of astrophysics and cosmology in the distant universe. However, in order to realize the potential of this new technique, we must improve our understanding of the link between intensity maps and physics on all scales. In this thesis, we develop a number of tools for analyzing intensity maps. For most of this work, we illustrate our methods using the example of a CO intensity map at redshift z ∼ 3, though our results are applicable to many other lines. We first present the formalism for computing intensity mapping power spectra, and use it to study the detectability of the CO signal. We find that, though the signal amplitude is highly uncertain, a well-designed experiment can detect CO under most assumptions. We then describe a method of simulating intensity maps, and use it to study the problem of foreground line contamination. By masking out the brightest regions of a map, we find that the cosmological information contained in surveys targeting Lyα and CO can be recovered, though astrophysical information is lost. Unfortunately, due to instrumental constraints and high-intensity foreground ii ABSTRACT contamination, this method is less effective for CII surveys. We also demonstrate that the astrophysical information content of foreground lines can be recovered through cross-correlations. By correlating pairs of CO frequency bands, we show that the 13CO isotopologue line can allow unprecedented constraints on molecular gas properties within distant galaxies. Line emission in intensity maps is highly non-Gaussian, so power spectra alone cannot fully constrain the properties of target populations. To resolve this, we intro- duce the Voxel Intensity Distribution (VID), which gives the one-point probability distribution of voxel intensities. We demonstrate that the VID provides substantial constraining power beyond what is obtainable from the power spectrum, even in the presence of foreground contamination. We then apply the VID to a hypothetical measurement of the cosmic star formation rate density from a CO survey. We show that, despite model uncertainties, such an observation could place competitive con- straints on this crucial quantity while eliminating systematics which hamper existing measurements. Primary Reader: Marc Kamionkowski Secondary Reader: Julian Krolik iii Acknowledgments First and formost I need to thank my Ph.D. advisor Marc Kamionkowski for all of his help and support over the past five years. He is a brilliant physicist anda fantastic mentor and I would not be the scientist I am today without his efforts. I am also grateful to my thesis defense committee, Julian Krolik, Toby Marriage, Rigoberto Hernandez, and Eric Switzer for taking the time to provide insightful and constructive comments about this work. I am indebted to my collaborators over the years, particularly to Ely Kovetz, Mubdi Rahman, and Yacine Ali-Ha¨ımoud,who have passed along their knowledge and experience on topics ranging from astrophysics to numerical methods to job ap- plications. I also need to thank the other members of Marc's group at JHU, including Donghui Jeong, Jens Chluba, Alvise Raccanelli, Simeon Bird, and Illias Cholis for sharing their knowledge over the years. I am extremely thankful for the companionship of the other graduate students at Hopkins. In particular I need to thank my officemates Alice Cocoros, Dan Pfeffer, Nick Eminizer, and Jon Aguilar for keeping me sane every day, as well as my fellow iv ACKNOWLEDGMENTS group members Julian Mu~noz,Tanvi Karwal, and Liang Dai. I also need to thank the myriad members of The Rabble for making Hopkins such a fun place to be. Rachael Alexandroff deserves particular thanks for any number of things, and for keeping me company on the upcoming move to Toronto. My family has been a constant source of support throughout my life, and in particular as I finish my Ph.D. and move into the academic world. My parents, Pat and Jill Breysse have always had my back, and my father in particular who, as a fellow academic, understands the challenges of an academic career and has been an invaluable source of counsel. Finally, I could not have accomplished any of this without my wife Heather, who has been my constant partner through college, grad school, and beyond. v Dedication This thesis is dedicated to my parents, Pat and Jill Breysse vi Contents Abstract ii Acknowledgments iv List of Tables xi List of Figures xii 1 Introduction 1 2 Intensity Mapping Power Spectra 12 2.1 3D Power Spectrum . 12 2.2 Angular Power Spectrum . 15 2.3 Summary . 17 3 CO at Moderate Redshifts 18 3.1 Modeling CO Emission . 19 3.1.1 Estimating A ........................... 19 vii CONTENTS 3.1.2 CO Power Spectra . 23 3.2 Dependence on Survey Design . 26 3.3 Summary . 33 4 Simulating Intensity Maps 37 4.1 Shot Noise Simulations . 38 4.2 Adding Clustering . 42 4.3 Summary . 44 5 Masking Line Foregrounds 46 5.1 Signal and Foreground Models . 49 5.1.1 CO . 51 5.1.2 Lyman α .............................. 56 5.1.3 CII . 58 5.2 Simulated Maps . 60 5.3 Voxel Masking . 63 5.4 Masking Results . 66 5.4.1 CO . 67 5.4.2 Lyman α .............................. 68 5.4.3 CII . 69 5.5 Discussion . 70 5.6 Summary . 74 viii CONTENTS 6 CO Cross-Correlations 76 6.1 Molecular Gas and 13CO . 77 6.2 Leveraging Isotopologues . 79 6.3 Formalism . 82 6.3.1 12CO/13CO Intensity Ratios . 83 6.3.2 Power spectra . 86 6.4 Forecast . 89 6.4.1 12CO Luminosity Function . 90 6.4.2 Relating Luminosity to Column Density . 92 6.4.3 Experimental Parameters . 94 6.4.4 Fisher analysis . 95 6.5 Discussion . 99 6.6 Summary . 104 7 One-Point Statistics 105 7.1 P (D) Formalism . 107 7.2 Fiducial CO Model . 114 7.3 Constraining Power . 115 7.4 Foreground Effects . 120 7.4.1 Continuum Foregrounds . 121 7.4.2 Line Foregrounds . 125 7.4.3 Gravitational Lensing . 132 ix CONTENTS 7.5 Discussion . 138 7.6 Summary . 144 8 Constraining the Cosmic Star-Formation History 146 8.1 CO-to-SFR Model . 148 8.2 SFRD Constraints . 152 8.3 Discussion . 157 8.4 Summary . 161 9 Conclusions 162 A Spurious 12CO Power 167 B Marginalizing over 12CO 170 C Luminosity-Dependent Bias 172 D Numerical and Analytic VIDs 175 E Constraints with Foregrounds 177 F Full FG2 Constraints 179 Vita 192 x List of Tables 5.1 Parameters of power law L(M) model for CO and its foreground lines 54 6.1 Survey parameters used for Fisher analysis. 94 7.1 Schechter model parameters for CO and hypothetical foregrounds . 127 xi List of Figures 1.1 Schematic view of the history of the universe, and the epochs probed by galaxy surveys, the CMB, and intensity mapping . 3 3.1 CO angular power spectra at different frequency bandwidths . 25 3.2 Effect of Limber approximation on C` . 25 3.3 SNR of a CO survey as a function of beam size and survey area . 29 3.4 SNR as a function of area for a CO survey with a 10 arcmin beam . 30 3.5 SNR as a function of A .......................... 30 3.6 SNR as a function of target redshift . 31 3.7 SNR as a function of number of frequency channels . 33 3.8 SNR as a function of survey time and area . 34 4.1 Simulated maps and power spectra of CO at z = 3 . 41 5.1 Theoretical power spectra for CO and possible foreground lines . 55 5.2 Theoretical power spectra for Lyα and its three foreground lines . 58 5.3 Theoretical power spectra for CII and its four foreground CO lines. 59 5.4 Simulated power spectra of CO and HCN . 61 5.5 Simulated maps of CO, Lyα, and CII along with their foregounds . 62 5.6 Number of sources/voxel in CO map which produce intensities greater than some temperature T ........................ 64 5.7 Effect of masking on foreground-contaminated CO power spectrum .68 5.8 Effect of masking on foreground-contaminated Lyα power spectrum . 69 5.9 Effect of masking on foreground-contaminated CII power spectrum .70 6.1 Schematic view of 12CO/13CO cross-correlation . 80 6.2 13CO/12CO intensity ratios as a function of column density . 87 6.3 Power spectra of 12CO and 13CO . 96 6.4 Fisher constraints on molecular gas . 99 7.1 VIDs computed for fiducial CO model . 117 xii LIST OF FIGURES 7.2 VID constraints on CO model parameters . 119 7.3 VID constraints on CO luminosity function . 120 7.4 Constraints on CO with continuum foregrounds . 124 7.5 Hypothetical foreground luminosity functions, VIDs, and power spectra 128 7.6 Constraints on CO with shot-noise foreground . 129 7.7 Constraints on CO with clustering foreground . 131 7.8 Magnification PDFs from strong and weak lensing . .136 7.9 Effect of lensing on the VID . .137 8.1 VIDs for CO-to-SFR model . 150 8.2 Constraints on CO mass-luminosity relation from VID . 153 8.3 Constraints on mean intensity and SFRD . 154 8.4 Predicted constraints on SFRD as a function of redshift . 155 8.5 VID constraints on star formation history compared to current mea- surements . 156 C.1 Effect of luminosity-dependent bias on VID .
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