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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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