Speaker
Ivan Kharuk
Description
Baikal-GVD is a large-scale underwater neutrino telescope in Lake Baikal designed to study the properties of high-energy neutrinos. In this talk, I will present the neural-network–based data processing chain currently under development for Baikal-GVD data analysis. This pipeline addresses the following goals: suppression of extensive air shower background, rejection of optical module activations caused by natural water luminescence, and reconstruction of neutrino energy and arrival direction. The developed methods improve Baikal-GVD's reconstruction accuracy and accelerate data processing. I will also discuss the challenges, including importance of domain adaptation, and outline directions for future developments.
