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Title:The distribution of dark matter at large scales and in gravitational lenses
Author(s):Wen, Di
Director of Research:Kemball, Athol J
Doctoral Committee Chair(s):Kemball, Athol J
Doctoral Committee Member(s):Fields, Brian D; Shen, Yue; Liu, Xin
Department / Program:Astronomy
Discipline:Astronomy
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):cosmology
dark matter
dark energy
counts-in-cells
large-scale structure
gravitational lensing
primordial black hole
flux ratio anomaly
Abstract:The nature of dark matter and dark energy is one of the most important unsolved problems in cosmology. The distribution of dark matter at cosmological scales, galactic scales and subgalactic scales all reveal complementary aspects of the physics of dark matter. The broadest set of probes for dark energy are necessary to constrain the dark energy equation of state and to distinguish dark energy models. In this thesis, I review the application of counts-in-cells probability distribution functions in measuring the clustering of large-scale structures in cosmology. I examine the counts-in-cells probability distribution functions that describe dark matter halos in the Dark Energy Universe Simulations (DEUS) and describe the measurements between redshifts $z=0$ to $z=4$ on both linear and non-linear scales. The best-fits of the gravitational quasi-equilibrium distribution (GQED), the negative binomial distribution (NBD), the Poisson-Lognormal distribution (PLN), and the Poisson-Lognormal distribution with a bias parameter (PLNB) are compared to simulations. The distributions agree reasonably well over a range of redshifts and scales. To distinguish quintessence (RPCDM) and phantom ($w$CDM) dark energy from $\Lambda$ dark energy, I present a new method that compares the model parameters of the counts-in-cells probability distribution functions. I find that the mean and variance of the halo counts-in-cells on $2-25h^{-1}$Mpc scales within a redshift range of $0.65
Issue Date:2020-12-03
Type:Thesis
URI:http://hdl.handle.net/2142/109630
Rights Information:Copyright 2020 Di Wen
Date Available in IDEALS:2021-03-05
Date Deposited:2020-12


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