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UC Berkeley UC Berkeley Electronic Theses and Dissertations Title Reconstruction of Cosmological Fields in Forward Model Framework Permalink https://escholarship.org/uc/item/4g05x2zh Author Modi, Chirag Publication Date 2020 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California Reconstruction of Cosmological Fields in Forward Model Framework by Chirag Modi A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Physics in the Graduate Division of the University of California, Berkeley Committee in charge: Professor UroˇsSeljak, Chair Professor Martin White Professor Fernando P´erez Summer 2020 Reconstruction of Cosmological Fields in Forward Model Framework Copyright 2020 by Chirag Modi 1 Abstract Reconstruction of Cosmological Fields in Forward Model Framework by Chirag Modi Doctor of Philosophy in Physics University of California, Berkeley Professor UroˇsSeljak, Chair The large scale structures (LSS) of the Universe contain a vast amount of information about the birth, evolution and composition of our Universe. To mine this information over the next decade, large scale imaging surveys such as the Dark Energy Spectroscopic Survey (DESI), Large Synoptic Survey Telescope (LSST), Euclid and WFIRST will probe the Universe with unprecedented precision, and on the largest scales. This provides an opportunity to shed light on the longstanding mysteries regarding the true nature of dark matter and dark energy, as well as to resolve tensions between different probes of the past decade. However to utilize the full potential these surveys which are no longer statistically limited, it is critical to develop analytic methods that extract the maximum amount of information across all scales. In this regard, the massive data volumes, the non-linearity, and non-Gaussianity of the signal in the regimes probed by these surveys will pose outstanding challenges. The focus of this thesis is to tackle aforementioned challenges with a novel framework for the analysis of the large scale structures by reconstructing cosmological fields using forward models. These forward modeling approaches are the most promising way to jointly model different cosmological observations with requisite accuracy at all scales, while accounting for their individual systematic biases and noise. We approach reconstruction within a Bayesian formalism - by optimizing the likelihood of the observed data and maximizing the cor- responding posterior of initial conditions. This requires solving optimization problems in multi-million dimensional space and we develop novel differentiable forward models as well as adiabatic optimization algorithms to assist with this. We begin by using this approach to reconstruct the initial density field from a continuous dark matter field, as well as discrete observables like dark matter halos. We also construct a power spectrum estimator for this reconstructed Gaussian density field, which is the summary statistic for optimal analysis. With 21-cm intensity mapping, we show how this framework can instead be used for secondary analysis such as de-noising the observed data and recov- ering lost cosmological information. For this, we also introduce Hidden Valley Simulations 2 - a suite of high resolution N-Body simulations and develop models to simulate HI cluster- ing. Having demonstrated the efficacy of this approach, we finally introduce FlowPM - a GPU-accelerated, distributed, and differentiable N-Body solver in TensorFlow that naturally interfaces with the most advanced machine learning tools. This will enable the community to efficiently solve large scale cosmological inference problems and develop differentiable for- ward models for the reconstruction of cosmological fields with the next generation of LSS surveys. i To my parents, Ajay and Nisha Modi, who with unconditional love, support, constant encouragement and sacrifices raised me to be the person I am today. You have always been an inspiration to me. And to my grandfather Sanat Kumar Modi, who always believed in me. I miss you Ba. ii Contents Contents ii List of Figures iv List of Tables xvi 1 Introduction1 1.1 Large Scale Structures .............................. 4 1.2 Modeling Growth and Evolution of Dark Matter................ 4 1.3 Modeling Halos and Galaxy Distribution.................... 8 1.4 Analysis...................................... 13 1.5 Other Probes of Large Scale Structures..................... 19 1.6 Outline of this Thesis............................... 21 2 Differentiable Forward Models in Cosmology 23 2.1 Particle Mesh Simulations ............................ 24 2.2 Bias Model .................................... 28 2.3 Lagrangian Bias at Field Level.......................... 34 2.4 Neural Network Model.............................. 35 3 Towards optimal extraction of cosmological information from nonlinear data 40 3.1 Introduction.................................... 40 3.2 Statistical approach and heuristic derivation.................. 43 3.3 Maximum a posteriori (MAP) initial mode estimator ............. 48 3.4 Minimum variance initial power spectrum estimator.............. 51 3.5 Application to dark matter simulations..................... 63 3.6 Conclusions.................................... 75 4 Cosmological Reconstruction From Galaxy Light: Neural Network Based Light-Matter Connection 79 4.1 Introduction.................................... 79 4.2 Neural Network Model.............................. 83 iii 4.3 Simulations .................................... 88 4.4 Model Performance................................ 88 4.5 Reconstruction .................................. 96 4.6 BAO Information................................. 110 4.7 Conclusions and Discussion ........................... 113 5 Reconstructing large-scale structure with neutral hydrogen surveys 116 5.1 Introduction.................................... 117 5.2 Setting the Stage................................. 120 5.3 Hidden Valley simulation suite.......................... 123 5.4 The HI model................................... 124 5.5 Validating Hidden Valley Simualtions...................... 132 5.6 Reconstruction of HI Field............................ 145 5.7 Results....................................... 150 5.8 Implications.................................... 161 5.9 Conclusions.................................... 169 6 FlowPM: Distributed TensorFlow Implementation of the FastPM Cos- mological N-body Solver 173 6.1 Introduction.................................... 173 6.2 The FastPM particle mesh N-body solver.................... 175 6.3 Multi-grid PM Scheme.............................. 176 6.4 Mesh-TensorFlow................................. 178 6.5 FlowPM: FastPM implementation in Mesh-Tensorflow . 180 6.6 Scaling and Numerical Accuracy......................... 184 6.7 Example application: Reconstruction of initial conditions . 190 6.8 Conclusion..................................... 198 7 Conclusions 201 Bibliography 203 iv List of Figures 1.1 Cosmic history and evolution of the Universe from Big Bang to the present day. Credits ESA ..................................... 2 1.2 Evolution of dark matter field under gravity. We show (top left) the initial Gaus- sian field at z = 9, (top right) Zeldovich evolution, (bottom left) 2LPT evolution and (bottom right) FastPM [83] particle mesh evolution with 20 time steps to z = 0. ......................................... 9 1.3 (Left) Real space matter power spectrum. with measurements from various sur- veys probing LSS of the Universe. Figure taken from [6]. (Right) Matter corre- lation function in real space. Figure taken from [60] ............... 14 1.4 Modeling matter power spectrum in real space at different redshifts. We show re- sults from N-Body simulation, along with linear theory, Eulerian and Lagrangian perturbation theories at different orders and Cosmic Emu emulator. Figure taken from [242]....................................... 14 1.5 (Left) Halo (red) and galaxy (blue) power spectrum in real space. Solid lines are perturbation theory fits and points are N-Body simulations. (Right) Halo and galaxy power spectra, monopole and quadrapole, in redshift space. FOG suppression on small scales is clearly visible in case of galaxies. Figure taken from [175]........................................ 16 1.6 Baryon acoustic oscillations in SDSS-BOSS survey, data release 12, between red- shift 0:5 < z < 0:7. Lines are best fits and markers are observed data points. (Left) BAO in the power spectrum as wiggles in the monopole, and in quadrapole. We separately show SGC and NGC data. Figure taken from [27] (Right) BAO as the peak in correlation function monopole and quadrapole. Figure taken from [199]........................................... 17 1.7 Damping and reconstruction of BAO (Top Left) Sharp BAO peak feature at the time of decoupling. (Top Right) Broadening of BAO peak due to displacement of galaxies under gravity. (Bottom Left) Estimated Zeldovich displacements to undo the non-linear evolution of the galaxies. (Bottom Right) Reconstructed density field and sharpened BAO peak. Figure taken from [179].......... 18 v 2.1 Bias parameters as function of k: We show b1, b2 and bs2 as measured from Fourier space estimator. For clarity, we only show 2 mass bins from every box and different line-styles (dashed, solid and dotted) correspond to different boxes (of size L = 690; 1380; 3000 Mpc=h respectively), from which the corresponding mass bins (specified by