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Probing Cosmology at Different Scales

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Probing Cosmology at Different Scales PROBING COSMOLOGY AT DIFFERENT SCALES by Daniel Pfeffer A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy. Baltimore, Maryland October, 2019 ⃝c 2019 Daniel Pfeffer All rights reserved Abstract Although we are in an era of precision cosmology, there is still much about our Universe that we do not know. Moreover, the concordance ΛCDM model of cosmology faces many challenges at all scales relevant to cosmology. Either discrepancies have been discovered with ΛCDM predictions or the model has simply broken down and is not a useful predictor. The current rate of expansion is incorrectly predicted by ΛCDM, as are the size and distribution of dwarf halo galaxies for Milky Way like galaxies. Obtaining the observed cores in galaxies with the standard description of dark matter is troublesome and requires more than the base ΛCDM model, as does understanding star formation and how it impacted galaxy evolution requires much more than base ΛCDM knowledge and many more. My work focuses on probing different scales in cosmology with different techniques to extract information about our Universe and its history. I use ultra-high-energy cosmic-rays (UHECRs) as a probe of the local universe and tested tidal disruption events (TDES) as a possible source of the UHECRs. By analyzing energy require- ii ABSTRACT ments, source densities and observed fluxes, I find that TDEs can explain the observed UHECR flux. The assumption of TDEs as the source of UHECRs can leadtoa measurement of the density of super massive black holes which reside in the center of galaxies. At a larger scale, I build a tool to extract the luminosity function of CO from star-forming galaxies with line intensity maps (LIMs) and convolutional neural networks (CNNs). This new technique allows a faster analysis of LIMs in a more model-independent way than previous techniques. Finally, at the largest observable scales, I probe potential dark matter interactions and their impact on the cosmic mi- crowave background (CMB). This work explores how different dark matter interaction mechanisms impact the CMB when considered simultaneously and individually. As cosmology is a science of many scales, all of these scales must be studied to improve our understanding of the Universe. Dong so, my thesis has wide-ranging implications for cosmic-rays, star formation and galaxy evolution, and dark matter interactions. iii ABSTRACT Readers Marc Kamionkowski (Primary Advisor) Emanuele Berti Brice Menard Marc Postman Andy Fruchter iv Acknowledgments It goes without saying that I would not be where I am right now if not for my advisor Marc Kamionkowski. You were always there if I had questions or issues and were very supportive in my shift away from academia at the end. Being in your group helped me grow as a scientist and expand my horizons and gave me the opportunity to learn from many other scientists as well. Speaking of the Kamionkowski group, I must thank all of the former and current postdocs who I have worked with. I have learned much from our work and interactions. You were always there to help out even when I was slow to grasp a concept or messed up my unit conversions repeatedly. Thank you Ely Kovetz, Simeon Bird, Ilias Cholis, Kimberly Boddy and Vivian Poulin. Many fellow graduate students have come and gone along this journey of mine. To my Bloomberg 423 office mates Patrick Breysse, Alice Cocoros, Bhaskar Balaji and Gabriela Sato-Polito, thank you for being there for stupid questions, goofing off with me and not commenting when I jam out to my music. To Patrick Breysse v ACKNOWLEDGMENTS (again) and Julian Mu~nozas more senior graduate students in the group, thank you for being another resource to the wonderful world of cosmology. To Tanvi Karwal, we have been in the same boat now for the past five years and it has been fun going through the entire experience with you even if we cannot work together without immediately yelling at each other. I also have to give a shout out to my lifting crew, Kim Berghaus, Michael Busch, Tanvi Karwal (again) and Lingyuan Ji. Even if I did not learn anything in the past couple of years, I have definitely gotten into much better shape. Of course I must thank Kelley Key. My time here would not have been the same without someone like you in the administration and as a friend. Finally I must thank my parents, Gerry Pfeffer and Cindy Virgil as well asmy brother Alex Pfeffer. I am where I am because of the interactions I had withyou three over the course of my life. You always encouraged me no matter which path I chose in life. vi Dedication To my loving parents and brother. I have the final proof that I am the smarter son. vii Contents Abstract ii Acknowledgments v List of Tables xi List of Figures xii 1 Introduction 1 2 TDEs and UHECR Hot Spots 9 2.1 The hot and warm spots . 12 2.2 Time scales and energetics . 14 2.3 Isotropic flux . 19 2.4 Discussion: TDE scorecard . 23 2.5 Conclusions . 25 3 LIM with Neural Networks 27 3.1 Simulated Maps . 34 viii CONTENTS 3.1.1 Dark Matter Simulations . 35 3.1.2 CO Modelling . 36 3.1.3 Noise and Foregrounds . 38 3.2 Convolutional Neural Network . 46 3.2.1 Network Architecture . 47 3.2.2 Implementation . 51 3.3 Results . 56 3.3.1 Tests on trained data . 59 3.3.2 Tests on untrained data . 65 3.4 Discussion . 70 3.5 Conclusion . 73 4 Interacting DM Signatures on the CMB 74 4.1 Modified CMB physics . 78 4.1.1 Dark matter scattering . 78 4.1.2 Dark matter annihilation . 81 4.2 Scattering and annihilation signals in the CMB . 83 4.2.1 Previous limits from Planck ....................... 84 4.2.2 Both effects from multiple couplings . 88 4.2.3 Both effects from scattering saturation . 91 4.3 Looking for electromagnetic signals of Dark Matter in CMB data . 94 4.3.1 Dipole dark matter . 95 4.3.2 Millicharge dark matter . 98 ix CONTENTS 4.4 Discussion/Conclusions . 104 Appendix 107 A.1 Benchmark models of dark matter . 107 A.1.1 Dipole dark matter . 107 A.1.2 Millicharge dark matter . 109 Bibliography 111 Vita 148 x List of Tables 3.1 Experiment setup for COMAP Phase one . 35 3.2 Summary of models used for Resnet testing . 40 3.3 Resnet Architecture . 51 3.4 Resnet accuracy . 68 xi List of Figures 3.1 Parameter space of luminosity function values used in training . 39 3.2 Line Intensity Map Slices with and without noise . 45 3.3 Residual block structures . 48 3.4 Set of residual blocks . 52 3.5 Full Resnet architecture diagram . 53 3.6 Training loss history . 55 3.7 Resnet example tests . 57 3.8 Resnet comparison to PS/VID technique . 61 3.9 Resnet relative error on simple LIMs . 62 3.10 Resnet relative error on random LIMs . 64 3.11 Resnet relative error on LIMs with untrained features . 67 3.12 Zoom in of Resnet relative errors . 69 4.1 Analytic predictions to determine which dark matter interaction dominates 86 4.2 Dipole dark matter CMB power spectra residuals . 88 4.3 Millicharge dark matter CMB power spectra residuals . 91 4.4 Dipole dark matter exclusion contour . 97 xii LIST OF FIGURES 4.5 Millicharge dark matter individual affect exclusion contours . 101 4.6 Millicharge dark matter exclusion contours . 102 xiii Chapter 1 Introduction Cosmology is a field of many scales with different physics governing these different scales. The universe itself is large, but its history and evolution vary based on what scale is being considered. Dwarf galaxies orbiting Milky-Way-like galaxies are described differently than the large-scale structure governing superclusters of galaxies. At still larger scales, cos- mologists study the cosmic microwave background (CMB), the oldest light in the Universe, the scale of which is the size of the observable Universe. At the other end of the spectrum, the physics of very small scale is also relevant to cosmology. The early universe was a much denser and hotter place and is described by particle physics. Therefore, in attempting to understand the nature of the universe, we must learn about physics at many scales. Due to its agreement with numerous data sets, the flat ΛCDM model has become the concordance model of cosmology [1{13]. Within ΛCDM there is radiation, baryon, cold dark matter (CDM) and dark energy. Radiation encompasses either photons or any relativistic 1 CHAPTER 1. INTRODUCTION species of matter and is negligible in terms of its impact on the universe today. Baryons or what people generally consider as normal matter make up roughly 5% of the energy density of the universe. A slightly different form of matter is cold dark matter which accounts for 25% of the energy density of the universe. It is cold because it has little to no thermal velocity and is dark because we do not observe it through interactions with photons [14,15], but rather only gravitationally [16{18]. The rest of the universe, around 70%, is in the form of dark energy which would explain the accelerated expansion of the universe [19{22]. Dark energy can take many forms, but the simplest model that is extremely successful is that it is a cosmological constant Λ. Although ΛCDM does an excellent job of describing the universe, it is not perfect and there is much room for improvement. Nearly perfect fluids are used in ΛCDM to describe the components of the Universe.
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