Neutrino Physic

Neutrino physics aims to understand the properties and behavior of neutrinos, elusive subatomic particles, primarily through the analysis of their interactions in massive detectors. Current research heavily utilizes machine learning, employing techniques like graph neural networks, convolutional neural networks, and normalizing flows to improve event reconstruction, energy resolution, and particle identification in diverse detector technologies (e.g., liquid scintillators, LArTPCs, ice). These advancements are crucial for enhancing the precision of neutrino oscillation measurements, probing fundamental physics beyond the Standard Model (such as lepton flavor violation), and furthering our understanding of astrophysical sources of high-energy neutrinos.