Flavor Neutrino Physic

Flavor neutrino physics explores the properties and interactions of neutrinos, focusing on how their "flavors" (electron, muon, and tau) change during propagation. Current research utilizes machine learning techniques, including neural networks (e.g., convolutional and graph neural networks) and reinforcement learning, to analyze complex datasets from experiments and simulations, improving neutrino reconstruction and parameter estimation in models beyond the Standard Model. These advancements are crucial for understanding fundamental physics, such as neutrino oscillations and the origin of neutrino masses, and have applications in astrophysics (e.g., supernova neutrino detection) and other fields.