Discrimination of Cosmic Rays and Neutrinos at Large Zenith Angles Using Machine Learning

Simulated data of neutrinos and protons were generated using the CORSIKA software across three energy ranges—100 TeV–1 PeV, 1 PeV–10 PeV, and 10 PeV–100 PeV—and four zenith angles: 60◦ , 70◦ , 80◦ , and 89◦ . For each event, secondary particles within 0–600 m from the shower axis were analyzed. Electromagnetic and muonic components were characterized using statistical moments of lateral distributions, NKG fit age parameters, particle counts, and the electron-to muon ratio. A stacking model was employed to build a classifier to distinguish between neutrino and proton events. Features were input into the model separately for each energy-angle bin. The classification performance was evaluated using the AUC score and 𝑄-factor. Results show strong classification capability across various energy and angle combinations, with AUC values generally exceeding 0.94 and 𝑄-factors reaching above 96, indicating effective signal-background separation. Further significance analysis incorporating flux estimates revealed that the detection significance exceeds 4.5𝜎 in most energy-angle bins, demonstrating the method’s potential for neutrino–proton discrimination and providing a reference framework for future neutrino observations aimed at exploring their astrophysical origins.