CIEMAT Publications

Abstract

This publication shows a part of the CMS detector upgrade within the future High Luminosity Large Hadron Collider (HL-LHC) project, scheduled for 2030, which aims to increase the LHC luminosity by a factor of 10. To select the most interesting collisions at the HL-LHC, improvements in the muon trigger algorithm are required. In this study, different machine learning algorithms are studied. They have been designed to enhance the performance of the trigger system by combining information from the Drift Tube (DT) and Resistive Plate Chamber (RPC) subdetectors. Neural networks capable of distinguishing particle tracks from background are employed and will be implemented in programmable logic devices, validating their feasibility for a hardware trigger system.