Göttingen 2025 – wissenschaftliches Programm

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T: Fachverband Teilchenphysik

T 76: Data, AI, Computing, Electronics VII (Generative AI, MC Generators)

T 76.9: Vortrag

Donnerstag, 3. April 2025, 18:15–18:30, VG 2.101

Geant4 Optimizations in ATLAS — •Mustafa Schmidt für die Mu2e Kollaboration — Bergische Universität Wuppertal, Deutschland

The ATLAS experiment at the LHC heavily depends on simulated event samples produced by a full Geant4 detector simulations. These Monte Carlo simulations based on Geant4 were a major consumer of computing resources during the 2018 data-taking year and will remain one of the dominant resource users in the HL-LHC era. Consequently, ATLAS has continuously been working to improve the computational performance of this simulation for the Run 3 Monte Carlo campaign.

This report highlights the recent implementation of Woodcock tracking in the Electromagnetic Endcap Calorimeter and provides an overview of other implemented and upcoming optimizations that still have to be validated. These improvements include enhancements to the core Geant4 software, strategic choices in simulation configuration, simplifications in geometry and magnetic field descriptions, as well as technical refinements in the interface between ATLAS simulation code and Geant4.

Overall, a performance improvement of around 50% regarding CPU time was achieved compared to the baseline simulation configuration utilized during Run 2.

Keywords: Monte-Carlo; ATLAS; Computing; Simulations; Optimization