Study of cosmic-ray composition with Imaging Atmospheric Cherenkov Telescopes

• Ano de defesa: 2024
• Autor(a) principal: Giler, Andres Gabriel Delgado
• 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: Air Shower; Aprendizado de Máquina; Chuveiro Atmosférico; Convolutional Neural Network; Imaging Atmospheric Cherenkov Telescope; Machine Learning; Profundidade do Máximo do Chuveiro; Rede Neural Convolucional; Shower Maximum
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/76/76134/tde-26042024-113543/

During the last three decades, the first and second generations of Imaging Atmospheric Cherenkov Telescopes (IACTs), such as Whipple Observatory, VERITAS, HESS and MAGIC, have provided measurements of several TeV gamma-ray sources. Experiments like the Cherenkov Telescope Array (CTA) will be the next-generation IACTs in the southern and northern hemispheres, offering better sensitivity, angular resolution, and larger collection area than the current generation. One of the CTAs aims is to make significant progress in detecting high-energy cosmic rays, providing insight into cosmic ray propagation and acceleration. The work done in this thesis is twofold. The first part proposes two methods to measure a mass-sensitive parameter of nuclei-initiated air showers: the depth of the shower maximum Xmax. The second part corresponds to the analysis of CTA simulations to separate iron-initiated from proton-initiated showers. This chapter will give a brief overview of all the results, ending with the conclusions of this thesis.