Application of Machine Learning Methods in Baikal-GVD: Background Noise Rejection and Selection of Neutrino-Induced Events

Abstract

Baikal-GVD is a large (\(\sim\)1 km\({}^{3}\)) underwater neutrino telescope located in Lake Baikal, Russia. In this report, we present two machine learning techniques developed for its data analysis. First, we introduce a neural network for an efficient rejection of noise hits, emerging due to natural water luminescence. Second, we develop a neural network for distinguishing muon- and neutrino-induced events. By choosing an appropriate classification threshold, we preserve \(90\%\) of neutrino-induced events, while muon-induced events are suppressed by a factor of \(10^{-6}\). Both of the developed neural networks employ the causal structure of events and surpass the precision of standard algorithmic approaches.