Machine Learning Techniques for Calorimetry

نویسندگان

چکیده

The Compact Muon Solenoid (CMS) is one of the general purpose detectors at CERN Large Hadron Collider (LHC), where products proton–proton collisions center mass energy up to 13.6 TeV are reconstructed. electromagnetic calorimeter (ECAL) crucial components CMS since it reconstructs energies and positions electrons photons. Even though several Machine Learning (ML) algorithms have been already used for calorimetry, with constant advancement field, more sophisticated techniques become available, which can be beneficial object reconstruction calorimeters. In this paper, we present two novel ML ECAL that based on graph neural networks (GNNs). new approaches show significant improvements compared current in CMS.

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ژورنال

عنوان ژورنال: Instruments

سال: 2022

ISSN: ['2410-390X']

DOI: https://doi.org/10.3390/instruments6040047