After the high-luminosity upgrade of the LHC, the muon chambers of CMS Barrel must cope with an increase in the number of interactions per bunch crossing. Therefore, new algorithmic techniques for data acquisition and processing will be necessary in preparation for such a high pile-up environment. Using Machine Learning as a technique to tackle this problem, this paper focuses in the production of models - with data obtained through Monte Carlo simulations - capable of predicting the transverse momentum of muons crossing the CMS Barrel muon chambers, comparing them with the transverse momentum (pT) assigned by the current CMS Level-1 trigger system.

Development of Machine Learning based muon trigger algorithms for the Phase2 upgrade of the CMS detector

Diotalevi, Tommaso;Bonacorsi, Daniele;Battilana, Carlo;Guiducci, Luigi
2018

Abstract

After the high-luminosity upgrade of the LHC, the muon chambers of CMS Barrel must cope with an increase in the number of interactions per bunch crossing. Therefore, new algorithmic techniques for data acquisition and processing will be necessary in preparation for such a high pile-up environment. Using Machine Learning as a technique to tackle this problem, this paper focuses in the production of models - with data obtained through Monte Carlo simulations - capable of predicting the transverse momentum of muons crossing the CMS Barrel muon chambers, comparing them with the transverse momentum (pT) assigned by the current CMS Level-1 trigger system.
2018
Development of Machine Learning based muon trigger algorithms for the Phase2 upgrade of the CMS detector
1
6
Diotalevi, Tommaso; Bonacorsi, Daniele; Battilana, Carlo; Guiducci, Luigi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/724428
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