Deep learning i tècniques bayesianes aplicades al big data en indústria i oscil·lacions de neutrins
- Autores: Sebastian Pina Otey
- Directores de la Tesis: Vicenc Gaitan Alcalde (dir. tes.), Maria del Pilar Casado Lechuga (codir. tes.), Thorsten Lux (codir. tes.)
- Lectura: En la Universitat Autònoma de Barcelona ( España ) en 2020
- Idioma: español
- Tribunal Calificador de la Tesis: Asher Kaboth (presid.), Inmaculada Riu Dachs (secret.), David Íñiguez Dieste (voc.)
- Programa de doctorado: Programa de Doctorado en Física por la Universidad Autónoma de Barcelona
- Materias:
- Enlaces
- Tesis en acceso abierto en: TDX
- Resumen
Neutrino oscillations are a complex phenomenon of theoretical and experimental interest in fundamental physics, studied through diverse experiments, such as the T2K Collaboration situated in Japan. T2K is composed of two facilities, which produce and measure neutrino interactions to get a better understanding of their oscillations through data analysis in the form of parameter inference, model simulation and detector response. Through this work, state-of-the-art deep learning techniques in the form of neural density estimators and graph neural networks will be applied and thoroughly verified in T2K use cases, assessing their benefits and shortcomings compared to traditional methods. Additionally an industrial usage of these methodologies for the Spanish electrical network will be discussed.
