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SUMMARY:Application of Machine Learning Methods for Event Reconstruction i
 n the SPD Range System
DTSTART;VALUE=DATE-TIME:20261029T063000Z
DTEND;VALUE=DATE-TIME:20261029T064500Z
DTSTAMP;VALUE=DATE-TIME:20261027T073642Z
UID:indico-contribution-6196@cern.ch
DESCRIPTION:Speakers: Alexandr Osetrov (JINR\, NRNU MEPhI)\nThe Joint Inst
 itute for Nuclear Research (JINR) is approaching the commissioning and ope
 rational readiness of the NICA (Nuclotron-based Ion Collider fAcility) meg
 a-science accelerator complex. One of its future detectors is the SPD (Spi
 n Physics Detector)\, designed to study the spin structure of nucleons. Th
 e Range System (muon system) is an integral part of the detector\, used fo
 r muon identification and coarse hadron calorimetry. This report is devote
 d to the simulation and reconstruction of events in the SPD Range System u
 sing the SpdRoot software framework. In particular\, it provides an overvi
 ew of machine learning algorithms\, such as DBSCAN\, BDT\, and CNN\, appli
 ed to clustering and particle classification tasks. These results provide 
 the foundation for critical measurements\, such as charmonium probing in t
 he SPD experiment.\n\nhttps://indico.particle.mephi.ru/event/626/contribut
 ions/6196/
LOCATION: Conference Hall 3
URL:https://indico.particle.mephi.ru/event/626/contributions/6196/
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