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Volume 321 - Sixth Annual Conference on Large Hadron Collider Physics (LHCP2018) - Posters
Development of Machine Learning based muon trigger algorithms for the Phase2 upgrade of the CMS detector
T. Diotalevi,* D. Bonacorsi, C. Battilana, L. Guiducci
*corresponding author
Full text: pdf
Pre-published on: 2018 October 18
Published on:
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 ($p_T$) assigned by the current CMS Level-1 trigger system.
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