Examinando por Autor "Aboulhorma A."

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    AtlFast3: The Next Generation of Fast Simulation in ATLAS
    (Springer Nature, 2022-12) Aad G.; Abbott B.; Abbott D.C.; Abud, A. Abed; Abeling K.; Abhayasinghe D.K.; Abidi S.H.; Aboulhorma A.; Abramowicz H.; Abreu H.; Abulaiti Y.; Hoffman, A. C.; Abuslemegj; Acharya B.S.; Achkar B.; Adam L.; Bourdarios, C. Adam; Adamczyk L.; Adamek L.; Addepalli S.V.; Adelman J.; Adiguzel A.; Adorni S.; Adye T.; Affolder A.A.; Afik Y.; Agapopoulou C.; Agaras M.N.; Agarwala J.; Aggarwal A.; Agheorghiesei C.; Aguilar-Saavedra J.A.; Ahmad A.; Ahmadov F.; Ahmed W.S.; Ai X.; Aielli G.; Aizenberg I.; Akatsuka S.; Akbiyik M.; Åkesson T.P.A.; Akimov A.V.; Khoury, K. Al; Alberghi G.L.; Albert J.; Albicocco P.; Verzini, M. J. Alconada; Alderweireldt S.; Aleksa M.; Aleksandrov I.N.; Alexa C.; Alexopoulos T.; Alfonsi A.; Alfonsi F.; Alhroob M.; Ali B.; Ali S.; Aliev M.; Alimonti G.; Allaire C.; Allbrooke B.M.M.
    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. © 2022, Springer Nature Switzerland AG.
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    Software Performance of the ATLAS Track Reconstruction for LHC Run 3
    (Computing and Software for Big Science, Volume 8, Issue 1December 2024 Article number 9, 2024-12) Aad G.; Abbott B.; Abeling K.; Abicht N.J.; Abidi S.H.; Aboulhorma A.; Abramowicz H.; Abreu H.; Abulaiti Y.; Acharya B.S.; Bourdarios, C. Adam; Adamczyk L.; Adamek L.; Addepalli S.V.; Addison M.J.; Adelman J.; Adiguzel A.; Adye T.; Affolder A.A.; Afik Y.; Agaras M.N.; Agarwala J.; Aggarwal A.; Agheorghiesei C.; Ahmad A.; Ahmadov F.; Ahmed W.S.; Ahuja S.; Ai X.; Aielli G.; Aikot A.; Tamlihat, M. Ait; Aitbenchikh B.; Aizenberg I.; Akbiyik M.; Åkesson T.P.A.; Akimov A.V.; Akiyama D.; Akolkar N.N.; Khoury, K. Al; Alberghi G.L.; Albert J.; Albicocco P.; Albouy G.L.; Alderweireldt S.; Aleksa M.; Aleksandrov I.N.; Alexa C.; Alexopoulos T.; Alfonsi F.; Algren M.; Alhroob M.; Ali B.; Ali H.M.J.; Ali S.; Alibocus S.W.; Aliev M.; Alimonti G.; Alkakhi W.; Allaire CAli S.; Alibocus S.W.; Aliev M.; Alimonti G.; Alkakhi W.; Allaire C
    Charged particle reconstruction in the presence of many simultaneous proton–proton (pp) collisions in the LHC is a challenging task for the ATLAS experiment’s reconstruction software due to the combinatorial complexity. This paper describes the major changes made to adapt the software to reconstruct high-activity collisions with an average of 50 or more simultaneous pp interactions per bunch crossing (pile-up) promptly using the available computing resources. The performance of the key components of the track reconstruction chain and its dependence on pile-up are evaluated, and the improvement achieved compared to the previous software version is quantified. For events with an average of 60pp collisions per bunch crossing, the updated track reconstruction is twice as fast as the previous version, without significant reduction in reconstruction efficiency and while reducing the rate of combinatorial fake tracks by more than a factor two. © The Author(s) 2024.