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SUMMARY:Reconstruction of neutrino-induced muon energy in Baikal-GVD with 
 transformer neural networks
DTSTART;VALUE=DATE-TIME:20261029T133500Z
DTEND;VALUE=DATE-TIME:20261029T135000Z
DTSTAMP;VALUE=DATE-TIME:20261029T174112Z
UID:indico-contribution-6208@cern.ch
DESCRIPTION:Speakers: Albert Matseiko (MIPT\, INR RAS)\nBaikal-GVD is a de
 ep underwater Cherenkov telescope at the bottom of Lake Baikal\, built to 
 detect high-energy astrophysical neutrinos. The energy of the neutrino is 
 one of the key parameters of a recorded event\, which makes its accurate m
 easurement an important task of the experiment.\n\nWe apply transformer ne
 ural networks to the reconstruction of the energy of a muon produced by a 
 muon neutrino\, in events confined to a single cluster of the telescope. T
 he energy is reconstructed at the point of the muon track where it enters 
 the sensitive volume of that cluster. The input of the network is the sequ
 ence of optical module hits recorded in the event\, and the network is tra
 ined on Monte Carlo simulation.\n\nTraining uses unsupervised domain adapt
 ation of the DANN type\, which prevents the network from distinguishing si
 mulation from experimental data. The loss function includes a negative log
 -likelihood term (beta-NLL)\, so that the network predicts not only the en
 ergy but also its own uncertainty for each event. Extension to the joint r
 econstruction of energy and muon track is planned.\n\nhttps://indico.parti
 cle.mephi.ru/event/626/contributions/6208/
LOCATION: Conference Hall 3
URL:https://indico.particle.mephi.ru/event/626/contributions/6208/
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