Abstract
Effective selection of muon candidates is the cornerstone of the LHC physics programme. The ATLAS experiment uses a two-level trigger system for real-time selection of interesting collision events. The first-level hardware trigger system uses the Resistive Plate Chamber detector (RPC) for selecting muon candidates in the central (barrel) region of the detector. With the planned upgrades, the entirely new FPGA-based muon trigger system will be installed in 2025-2026. In this paper, neural network regression models are studied for potential applications in the new RPC trigger system. A simple simulation model of the current detector is developed for training and testing neural network regression models. Effects from additional cluster hits and noise hits are evaluated. Efficiency of selecting muon candidates is estimated as a function of the transverse muon momentum. Several models are evaluated and their performance is compared to that of the current detector, showing promising potential to improve on current algorithms for the ATLAS Phase-II barrel muon trigger upgrade.
Highlights
The ATLAS experiment at the Large Hadron Collider (LHC) is a general purpose detector observing high energy collisions of protons and heavy ions
This paper develops fieldprogrammable gate arrays devices (FPGAs)-based neural network regression models for the new L0 muon trigger system
The preliminary results presented in this contribution show promising potential for more precise measurements of muon candidate pT using the neural network regression model with the ATLAS Resistive Plate Chamber detector (RPC) detector data
Summary
The ATLAS experiment at the Large Hadron Collider (LHC) is a general purpose detector observing high energy collisions of protons and heavy ions. The ATLAS physics programme includes measurements of the the Higgs boson properties, discovered simultaneously with CMS in 2012 [1, 2], measurements of the Standard Model properties, and many diverse searches for new physics phenomena. Many of these measurements require efficient detection of targeted physics processes with small cross-sections. Efficient selection of muon candidates is the important requirement of the ATLAS physics programme. This paper investigates new ideas for hardware-based trigger algorithms for identification of muon candidates
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