Synthetic Lyman-α quasar spectra datasets as a tool for cosmological studies

Abstract

The ΛCDM model has been one of the most successful theory for describing our Universe, explaining phenomena such as the accelerated expansion of the Universe, the Cosmic Microwave Background (CMB), and the large-scale structure (LSS). Among the various test of the ΛCDM model, the Baryon Acoustic Oscillations (BAO) stand out as an important probe. BAO originated during the first stages of the Universe evolution due to photon-baryon interactions and the competition between gravity and leave thermal pressure, leaving an important imprint in the clustering of matter that serves as a standard ruler for measuring cosmological distances. Galaxy spectroscopic surveys provide a great opportunity to measure the BAO imprint in the distribution of galaxies. The Dark Energy Spectroscopic Instrument (DESI) is the most recent effort on this kind of surveys, expecting to measure the spectra of approximately 40 million galaxies including approximately 700 thousand Lyman-α quasars, which include a series of absorption features caused by neutral hydrogen, known as the Lyman-α forest that can be used to measure the BAO scale at high redshifts. This thesis focuses on the Lyman-α forest, highlighting its importance as a matter distribution tracer and its role on studying the cosmic expansion history through the BAO scale. Furthermore, the main goal of this thesis is to provide the description of the methodology for generating synthetic Lyman-α spectra datasets and showcase their role for testing and improve various cosmological study methods involving the Lyman-α forest. The main contribution of this thesis is the development of a flexible and adaptable methodology to create realistic synthetic datasets, and that resulted in the production of 150 Lyman-α mocks that were used for the DESI first year (DESI-DR1) analysis. This thesis also highlights with examples the power of these synthetic datasets for validating of analysis, characterizing systematics, testing algorithms, and forecasting.