Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe
thesisposted on 2020-12-17, 21:38 authored by Collin Politsch
Modern astronomical surveys compile massive catalogs of images, light curves, and spectra that allow us to study the Universe from the relatively local (e.g. stars and extrasolar planets) to the extremely remote (e.g. quasars and the cosmic microwave background). As the magnitude of these catalogs continues to grow exponentially, the presence of statisticians and computer scientists working at the interface of astronomy and astrophysics becomes increasingly essential to the advancement of the field. In this dissertation, we study a variety of astrophysical problems of a statistical nature.
In Chapters 2 and 3, we introduce trend filtering (Tibshirani, 2014) into the astronomical literature and demonstrate its broad utility by discussing how it can contribute to a variety of spectroscopic and time-domain studies. The astronomical observations we discuss are (1) the Lyman-α forest absorptions in the spectra of high redshift quasars; (2) the broader spectroscopic signatures of quasars, galaxies, and stars; (3) stellar light curves with planetary transits; (4) light curves of eclipsing binary star systems; and (5) supernova light curves. We study the Lyman-α forest in the greatest detail — using trend filtering to map the large-scale structure of the intergalactic medium along one-dimensional quasar-observer sightlines. The remaining studies share broad themes of: (1) estimating observable parameters of light curves and spectra; and (2) constructing observational spectral/light-curve templates.
In Chapters 4 and 5, we continue our Lyman-α absorption spectroscopy analysis of the intergalactic medium by utilizing the full redshift z ≳ 2.1 quasar catalog compiled by the Baryon Oscillation Spectroscopic Survey (Dawson et al., 2013) to reconstruct a 47 h-3 Gpc3 three-dimensional large-scale structure map of the high redshift intergalactic medium — the largest volume map of the Universe to date — from the dense collection of one-dimensional quasar sightlines. We accompany the map with rigorous statistical error quantification and compile an extensive census of candidates for never-before-seen galaxy protoclusters and cosmic voids. The statistical reconstruction requires minimal assumptions on the underlying matter density field and is specifically optimized to recover three-dimensional structures lying between the one-dimensional sightlines backlit by quasars.
This Thesis has been approved by the Department of Statistics & Data Science and the Machine Learning Department for the joint Doctor of Philosophy degree in Statistics and Machine Learning
- Doctor of Philosophy (PhD)