Neutrino Angular Distribution

Neutrino angular distribution research focuses on understanding the directional properties of neutrinos, crucial for interpreting observations from various sources like supernovae and particle colliders. Current efforts leverage machine learning, employing neural networks (including convolutional and graph neural networks) and generative models to improve neutrino event reconstruction and parameter estimation from complex detector data. These advancements enhance our ability to analyze neutrino interactions, leading to more precise measurements of neutrino properties and improved understanding of fundamental physics, such as neutrino oscillations and lepton flavor violation.