Méthode Deep Learning and analyse Double Calorimétrique pour la mesure de haute précision des paramètres d’oscillation des neutrinos dans JUNO

JUNO is a multipurpose, mediumbaseline (≈52 km) liquid scintillator neutrino observatory located in China. Its primary objectives are to measure the oscillation parameters θ₁₂, Δm²₂₁ et Δm²₃₁ with per mil precision and to determine the neutrino mass ordering at a 3s confidence level. Achieving these goals requires an unprecedented energy resolution of 3%/ √E(MeV) with this technology. This demands a comprehensive understanding of the various effects within thedetector. The Dual Calorimetry system-two distinct measurement systems observing the same event-enables not only high-precision calibration but also detection of detector effects, as demonstrated in this thesis. Deep learning, an increasingly powerful tool in physics, plays a critical role in this effort. In this thesis, I present the development, application, and analysis of Deep Learning techniques for reconstruction in the JUNO experiment.