HI cosmology and advanced observational tools

• Ano de defesa: 2025
• Autor(a) principal: Motta, Pablo Cesar Benevides de Carvalho Rossas
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: cosmologia; radio-atronomia; telescópios; parâmetros cosmológicos; aprendizado de máquina; cosmology; radio astronomy; telescopes; cosmological parameters; machine learning
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/43/43134/tde-29092025-125747/

Neutral hydrogen (HI) intensity mapping is a promising technique for studying the evolution of the large-scale structure (LSS) of the Universe. The Baryon Acoustic Oscillations from Integrated Neutral Gas Observations (BINGO) experiment was designed to carry out this type of observation. In this work, we present forecasts demonstrating BINGOs potential to constrain cosmological parameters, both within the standard LCDM model and in scenarios with dynamical dark energy. Using simulations that account for instrumental noise, resolution, and foregrounds, we show that combining BINGO data with Planck satellite observations significantly improves cosmological constraints. Beyond its cosmological goals, BINGO is being expanded to detect fast radio transients, such as Fast Radio Bursts (FRBs), through the BINGO/ABDUS extension. This phase includes the addition of auxiliary radio telescopes equipped with Vivaldi antenna arrays operating between 0.5 and 1.5 GHz. Each tile can form multiple independent beams, enabling wide-area sky monitoring. Simulations indicate that the system has adequate sensitivity and resolution for detecting and localizing transients, making BINGO/ABDUS a promising tool for FRB studies and enhancing the overall scientific reach of the experiment. To support future joint opticalradio analyses, we also explore the use of photometric surveys to identify quasars, which trace the cosmic structure. We construct a catalog by cross-matching data from the Dark Energy Survey (DES) with spectroscopic classifications from the Sloan Digital Sky Survey (SDSS). Machine learning techniques are used to classify quasars and estimate photometric redshifts based on optical colors. The resulting catalog will serve as a basis for cross-correlation analyses with BINGO data.