Fast Implementation of Nonlinear Perturbation Theory Statistics Including Galaxy Bias and Redshift-space Distortion
Open Access
- Author:
- Tomlinson, Joseph
- Graduate Program:
- Astronomy and Astrophysics
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- August 21, 2024
- Committee Members:
- Kohta Murase, Outside Field Member
Sarah Shandera, Outside Unit Member
Michael Eracleous, Major Field Member
Donghui Jeong, Chair & Dissertation Advisor
Randy McEntaffer, Professor in Charge/Director of Graduate Studies - Keywords:
- Cosmology
Large-scale Structure - Abstract:
- One of the most powerful probes of cosmology in the current era are galaxy redshift surveys. While less precise than Cosmic Microwave Background (CMB) measurements, these surveys allow us to probe redshifts that are not simply accessible from the CMB. Fully grasping the data from these surveys is essential to gaining a deeper understanding of the Hubble tension, the evolution of dark energy, and other cosmological unknowns. Unlike the CMB, galaxies are formed at much later times, when the underlying matter density field is significantly nonlinear. These nonlinear features are both expensive to calculate and how much extra domain is gained is heavily dependent on redshift. We will first summarize the current state of Next-to-leading Order (NLO) power spectrum models and then describe how to extend these methods to the NLO bispectrum (a higher-order statistic compared to the power spectrum.) In addition to traditional nonlinear effects, the standard cosmological models only deal with modelling the underlying matter density field which is subtly different from what is actually observed in galaxy surveys, the galaxy density field. This difference between the galaxy and matter density, named galaxy bias, must be taken into account for more accurate modelling of past and future galaxy surveys. We will summarize a general method for determining all relevant biasing factors at a given order in perturbation theory and then develop how to apply these biasing factors to the fast model for the NLO power spectrum.