Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
AtlFast3: The Next Generation of Fast Simulation in ATLAS. / The ATLAS collaboration.
в: Computing and Software for Big Science, Том 6, № 1, 7, 12.2022.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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TY - JOUR
T1 - AtlFast3: The Next Generation of Fast Simulation in ATLAS
AU - The ATLAS collaboration
AU - Aad, G.
AU - Abbott, B.
AU - Abbott, D. C.
AU - Abud, A. Abed
AU - Abeling, K.
AU - Abhayasinghe, D. K.
AU - Abidi, S. H.
AU - Aboulhorma, A.
AU - Abramowicz, H.
AU - Abreu, H.
AU - Abulaiti, Y.
AU - Hoffman, A. C.Abusleme
AU - Acharya, B. S.
AU - Achkar, B.
AU - Adam, L.
AU - Bourdarios, C. Adam
AU - Adamczyk, L.
AU - Adamek, L.
AU - Addepalli, S. V.
AU - Adelman, J.
AU - Adiguzel, A.
AU - Adorni, S.
AU - Adye, T.
AU - Affolder, A. A.
AU - Afik, Y.
AU - Agapopoulou, C.
AU - Agaras, M. N.
AU - Agarwala, J.
AU - Aggarwal, A.
AU - Agheorghiesei, C.
AU - Aguilar-Saavedra, J. A.
AU - Ahmad, A.
AU - Ahmadov, F.
AU - Anisenkov, A. V.
AU - Baldin, E. M.
AU - Beloborodov, K.
AU - Bobrovnikov, V. S.
AU - Buzykaev, A. R.
AU - Kazanin, V. F.
AU - Kharlamov, A. G.
AU - Kharlamova, T.
AU - Maslennikov, A. L.
AU - Maximov, D. A.
AU - Peleganchuk, S. V.
AU - Podberezko, P.
AU - Rezanova, O. L.
AU - Soukharev, A. M.
AU - Talyshev, A. A.
AU - Tikhonov, Yu A.
AU - Zhulanov, V.
N1 - Funding Information: We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; ANID, Chile; CAS, MOST and NSFC, China; Minciencias, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRI, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; JINR; MES of Russia and NRC KI, Russian Federation; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, UK; DOE and NSF, USA. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; COST, ERC, ERDF, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d’Avenir Labex, Investissements d’Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; Norwegian Financial Mechanism 2014-2021, Norway; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, UK. Publisher Copyright: © 2022, Springer Nature Switzerland AG.
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85126227550&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/051f9cd8-ced1-3c21-82d5-89d70129b748/
U2 - 10.1007/s41781-021-00079-7
DO - 10.1007/s41781-021-00079-7
M3 - Article
AN - SCOPUS:85126227550
VL - 6
JO - Computing and Software for Big Science
JF - Computing and Software for Big Science
SN - 2510-2044
IS - 1
M1 - 7
ER -
ID: 35689950