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SUMMARY:Graph Attention Neural Network for Clustering Particle Tracks by E
 vents in the SPD experiment
DTSTART;VALUE=DATE-TIME:20261029T070000Z
DTEND;VALUE=DATE-TIME:20261029T071500Z
DTSTAMP;VALUE=DATE-TIME:20261019T042228Z
UID:indico-contribution-6199@cern.ch
DESCRIPTION:Speakers: Savelii Omelianchuk ()\nA Graph Attention Neural Net
 work (GANN) for particle track clustering by events in time slices obtaine
 d at the Spin Physics Detector (SPD) of the Nuclotron-based Ion Collider f
 Acility (NICA) is presented. A novel approach is applied to time slice pro
 cessing at high particle multiplicity and pile-up. The model is trained us
 ing a hierarchical approach and evaluated on time slices generated by the 
 Monte Carlo technique according to the SPD experiment configuration as clo
 se as possible at the moment. The model architecture includes a graph enco
 der and an edge classifier\, both of which use the message-passing princip
 le and iteratively structured approach. The GANN exhibits high performance
  in track clustering by events for the SPD simulation\, achieving 90% accu
 racy and 90% precision and recall.\n\nhttps://indico.particle.mephi.ru/eve
 nt/626/contributions/6199/
LOCATION:Sevastopol Hotel
URL:https://indico.particle.mephi.ru/event/626/contributions/6199/
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