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Publication Detail
AtlFast3: The Next Generation of Fast Simulation in ATLAS
  • Publication Type:
    Journal article
  • Authors:
    Aad G, Abbott B, Abbott DC, Abud AA, Abeling K, Abhayasinghe DK, Abidi SH, Aboulhorma A, Abramowicz H, Abreu H, Abulaiti Y, Hoffman ACA, Acharya BS, Achkar B, Adam L, Bourdarios CA, Adamczyk L, Adamek L, Addepalli SV, Adelman J, Adiguzel A, Adorni S, Adye T, Affolder AA, Afik Y, Agapopoulou C, Agaras MN, Agarwala J, Aggarwal A, Agheorghiesei C, Aguilar-Saavedra JA, Ahmad A, Ahmadov F, Ahmed WS, Ai X, Aielli G, Aizenberg I, Akatsuka S, Akbiyik M, Åkesson TPA, Akimov AV, Khoury KA, Alberghi GL, Albert J, Albicocco P, Verzini MJA, Alderweireldt S, Aleksa M, Aleksandrov IN, Alexa C, Alexopoulos T, Alfonsi A, Alfonsi F, Alhroob M, Ali B, Ali S, Aliev M, Alimonti G, Allaire C, Allbrooke BMM, Allport PP, Aloisio A, Alonso F, Alpigiani C, Camelia EA, Estevez MA, Alviggi MG, Coutinho YA, Ambler A, Ambroz L, Amelung C, Amidei D, Santos SPAD, Amoroso S, Amos KR, Amrouche CS, Ananiev V, Anastopoulos C, Andari N, Andeen T, Anders JK, Andrean SY, Andreazza A, Angelidakis S, Angerami A, Anisenkov AV, Annovi A, Antel C, Anthony MT, Antipov E, Antonelli M, Antrim DJA, Anulli F, Aoki M, Pozo JAA, Aparo MA, Bella LA, Aranzabal N, Ferraz VA, Arcangeletti C
  • Publisher:
    Springer Science and Business Media LLC
  • Publication date:
    01/12/2022
  • Journal:
    Computing and Software for Big Science
  • Volume:
    6
  • Issue:
    1
  • Article number:
    7
  • Status:
    Accepted
  • Language:
    English
  • Keywords:
    hep-ex, hep-ex
  • Notes:
    Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Abstract
The ATLAS experiment at the Large Hadron Collider has a broad physics programme ranging from precision measurements to direct searches for new particles and new interactions, requiring ever larger and ever more accurate datasets of simulated Monte Carlo events. Detector simulation with Geant4 is accurate but requires significant CPU resources. Over the past decade, ATLAS has developed and utilized tools that replace the most CPU-intensive component of the simulation—the calorimeter shower simulation—with faster simulation methods. Here, AtlFast3, the next generation of high-accuracy fast simulation in ATLAS, is introduced. AtlFast3 combines parameterized approaches with machine-learning techniques and is deployed to meet current and future computing challenges, and simulation needs of the ATLAS experiment. With highly accurate performance and significantly improved modelling of substructure within jets, AtlFast3 can simulate large numbers of events for a wide range of physics processes.
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