AtlFast3: The Next Generation of Fast Simulation in ATLAS
Metadatos
Afficher la notice complèteEditorial
Springer Nature
Date
2022-03-11Referencia bibliográfica
Aad, G., Abbott, B., Abbott, D.C. et al. AtlFast3: The Next Generation of Fast Simulation in ATLAS. Comput Softw Big Sci 6, 7 (2022). [https://doi.org/10.1007/s41781-021-00079-7]
Patrocinador
ANID; BSF-NSF; CEA-DRF; Cantons of Bern and Geneva; Czech Republic; DNSRC IN2P3-CNRS; EU-ESF; GIF, Israel; GenT Programmes Generalitat Valenciana, Spain; IRFU; La Caixa Banking Foundation; MES of Russia; MESTD; MIZŠ; MSMT; MSSR; Norwegian Financial Mechanism 2014-2021; PROMETEO; RGC; VSC CR; Wallenberg Foundation; National Science Foundation; U.S. Department of Energy; Alexander von Humboldt-Stiftung; Canarie; H2020 Marie Skłodowska-Curie Actions; Arizona-Nevada Academy of Science; ANAS; CERN; Compute Canada; Göran Gustafssons Stiftelser; Natural Sciences and Engineering Research Council of Canada; National Research Council Canada; Canada Foundation for Innovation; Leverhulme Trust; Royal Society; European Research Council; European Cooperation in Science and Technology; Australian Research Council; Neurosurgical Research Foundation; Singapore Eye Research Institute; Helmholtz-Gemeinschaft; Deutsche Forschungsgemeinschaft; Agence Nationale de la Recherche; Japan Society for the Promotion of Science; Ministry of Education, Culture, Sports, Science and Technology; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Danmarks Grundforskningsfond; Fundação de Amparo à Pesquisa do Estado de São Paulo; National Natural Science Foundation of China; Fundação para a Ciência e a Tecnologia; Bundesministerium für Bildung und Forschung; Chinese Academy of Sciences; Austrian Science Fund; Generalitat de Catalunya; Agencia Nacional de Promoción Científica y Tecnológica; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Bundesministerium für Wissenschaft, Forschung und Wirtschaft; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Joint Institute for Nuclear Research; Nella and Leon Benoziyo Center for Neurological Diseases, Weizmann Institute of Science; Israel Science Foundation; Instituto Nazionale di Fisica Nucleare; Narodowe Centrum Nauki; Javna Agencija za Raziskovalno Dejavnost RS; Ministerstwo Edukacji i Nauki; Ministry of Science and Technology, Taiwan; Ministerio de Ciencia e Innovación; Centre National pour la Recherche Scientifique et Technique; Horizon 2020; British Columbia Knowledge Development Fund; European Regional Development Fund; Defence Science Institute; Council on grants of the President of the Russian Federation; National Research Center "Kurchatov Institute"Résumé
The ATLAS experiment at the Large Hadron
Collider has a broad physics programme ranging from precision
measurements to direct searches for new particles and
newinteractions, 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 themostCPU-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 theATLAS experiment.With highly accurate performance
and significantly improved modelling of substructure within
jets,AtlFast3 can simulate large numbers of events for awide
range of physics processes.