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SUMMARY:Improvement of the energy resolution for neutral hadrons in highly
  granular calorimeters based on a neural network approach
DTSTART;VALUE=DATE-TIME:20221201T144500Z
DTEND;VALUE=DATE-TIME:20221201T150000Z
DTSTAMP;VALUE=DATE-TIME:20260916T181514Z
UID:indico-contribution-3132@cern.ch
DESCRIPTION:Speakers: Sergey Korpachev (LPI)\nThe technology of highly gra
 nular calorimeters is one of the main innovations that will be implemented
  in planned experiments on future colliders. This work shows an algorithm 
 for the improvement of energy resolution in highly granular calorimeters b
 ased on a machine learning technique. An artificial neural network\, which
  helps to connect calorimeter observables\, was trained and tested. The st
 udy was performed on a simulated version of the detector with highly granu
 lar calorimeters for single hadrons with energies from 1 to 120 GeV.\n\nht
 tps://indico.particle.mephi.ru/event/275/contributions/3132/
LOCATION:Hotel Intourist Kolomenskoye 4* Moskvorechye 1
URL:https://indico.particle.mephi.ru/event/275/contributions/3132/
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