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SUMMARY:Deep Learning for Fast Simulation of the Microstrip GEM Detector i
 n the BM@N Experiment
DTSTART;VALUE=DATE-TIME:20261029T074500Z
DTEND;VALUE=DATE-TIME:20261029T080000Z
DTSTAMP;VALUE=DATE-TIME:20261026T004614Z
UID:indico-contribution-6202@cern.ch
DESCRIPTION:Speakers: Dmitry Baranov (JINR)\nThe talk presents the first e
 xperience of applying deep neural networks to simulate the responses of mi
 crostrip GEM detectors used in the BM@N experiment at the NICA accelerator
  complex (JINR). The preparation of the training dataset based on data obt
 ained using classical Monte-Carlo simulation methods implemented in the BM
 NROOT software is discussed. The advantages of the neural network approach
  are considered\, particularly the reduction in detector response generati
 on time compared to traditional methods. The quality of the generated resp
 onses is also compared with reference simulations.\n\nhttps://indico.parti
 cle.mephi.ru/event/626/contributions/6202/
LOCATION: Conference Hall 3
URL:https://indico.particle.mephi.ru/event/626/contributions/6202/
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