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SUMMARY:Neural networks for data analysis in Baikal-GVD
DTSTART;VALUE=DATE-TIME:20261029T145000Z
DTEND;VALUE=DATE-TIME:20261029T150500Z
DTSTAMP;VALUE=DATE-TIME:20261025T181246Z
UID:indico-contribution-6217@cern.ch
DESCRIPTION:Speakers: Ivan Kharuk (INR & MIPT)\nWe present an overview of 
 the deep learning pipeline developed for data processing at the Baikal-GVD
  experiment. The pipeline covers the complete data analysis chain\, from n
 oise hit suppression to the reconstruction of neutrino arrival directions 
 and energies. By capturing spatial and temporal correlations across optica
 l modules\, Transformer and Graph Neural Network (GNN) architectures consi
 stently outperform conventional algorithmic reconstruction across all task
 s. Furthermore\, we discuss architectural enhancements — such as master 
 nodes\, fully connected output layers\, and loop structures — that boost
  overall reconstruction performance.\n\nhttps://indico.particle.mephi.ru/e
 vent/626/contributions/6217/
LOCATION: Conference Hall 3
URL:https://indico.particle.mephi.ru/event/626/contributions/6217/
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