Thesis

Deep learning and Bayesian techniques applied to big data in industry and neutrino oscillations

by Sebastian Pina-Otey (IFAE)

Europe/Madrid
IFAE seminar room (IFAE)

IFAE seminar room

IFAE

Description

Thesis supervisors:

Thorsten Lux, Vicens Gaitan and María Pilar Casado Lechuga

 

Abstract: 

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.

 

Streaming through the IFAE Zoom Seminar Room.

 

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