AtlFast3: Fast Simulation in ATLAS for LHC Run 3 and Beyond
Paper in proceeding, 2025
As we are approaching the high-luminosity era of the LHC, the computational requirements of the ATLAS experiment are expected to increase significantly in the coming years. Notably, simulation of Monte Carlo (MC) events is immensely computationally demanding, and their limited availability is one of the major sources of systematic uncertainties in many physics analyses. The main bottleneck in detector simulation is the detailed simulation of electromagnetic and hadronic showers in the ATLAS calorimeter system using Geant4. To increase MC statistics and to leverage the available CPU resources for LHC Run 3, the ATLAS Collaboration has recently put into production a refined and significantly improved version of its state-of-the-art fast simulation tool AtlFast3. AtlFast3 uses classical parametric and machine learning-based approaches such as Generative Adversarial Networks (GANs) for fast simulation of LHC events in the ATLAS detector. This work presents the newly improved version of AtlFast3 that is currently in production for simulation of Run 3 samples. In addition, ideas and plans for the future of fast simulation in ATLAS are also discussed.
Author
Federico Andrea Guillaume Corchia
National Institute for Nuclear Physics
University of Bologna
Marilena Bandieramonte
University of Pittsburgh
Joshua Falco Beirer
CERN
John Derek Chapman
University of Cambridge
Michael Dührssen-Debling
CERN
Florian Ernst
CERN
Heidelberg University
Michele Faucci Giannelli
Chalmers, Microtechnology and Nanoscience (MC2), Quantum Technology
Tong Qiu
University of Edinburgh
Jana Schaarschmidt
University of Washington
Firdaus Soberi
University of Edinburgh
Rui Zhang
Nanjing University
EPJ Web of Conferences
21016275 (ISSN) 2100014X (eISSN)
Vol. 337 01355
27th International Conference on Computing in High Energy and Nuclear Physics, CHEP 2024
Krakow, Poland,
Krakow, Poland,
Subject Categories (SSIF 2025)
Subatomic Physics
DOI
10.1051/epjconf/202533701355