Göttingen 2025 – wissenschaftliches Programm

Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe

T: Fachverband Teilchenphysik

T 77: Data, AI, Computing, Electronics VIII (Fast ML, Triggers)

T 77.1: Vortrag

Donnerstag, 3. April 2025, 16:15–16:30, VG 2.102

Optimization of the muon momentum resolution in the ATLAS first-level trigger with machine learning techniques — •Francisco Resende, Davide Cieri, Oliver Kortner, and Sandra Kortner — Max-Planck-Institut für Physik, München

The ATLAS experiment is upgrading its muon trigger system for operation at the High-Luminosity LHC. The necessary significant improvement in the selectivity of muon tracks within the first-level trigger relies on, for the first time, muon tracking data from precision monitored drift-tube (MDT) chambers.

This research explores the feasibility and benefits of integrating machine learning into the challenging real-time environment of the ATLAS trigger system, aiming to enhance the experiment's discovery potential in the high-luminosity era. We investigate the use of machine learning algorithms to improve muon reconstruction for the ATLAS first-level trigger. Various neural network models were developed, with algorithms optimized for potential deployment on powerful FPGA devices. The performance of each model is evaluated and compared to that of the baseline analytic algorithm in terms of trigger efficiency and muon momentum resolution.

Keywords: TDAQ; Drift-Tube; Neural-Network; ATLAS; Trigger