Speaker
Aleksey Leonov
(MIPT, INR)
Description
This study focuses on the reconstruction of neutrino direction in the Baikal-GVD experiment using convolutional neural networks and graph neural networks. Monte Carlo simulation data is utilized, examining single-cluster events of atmospheric neutrino with energies ranging from 10 GeV to 100 TeV. The performance of proposed models are compared to a standard reconstruction algorithm comparing their median angular resolutions. Results show that neural networks offer enhanced accuracy over the standard algorithm, particularly in small polar angles.
Primary author
Aleksey Leonov
(MIPT, INR)
Co-authors
Mr
Ivan Kharuk
(INR)
Mr
Oleg Kalashev
(INR)
