Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/11652
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Aad, G. | - |
dc.contributor.author | Abbott, B. | - |
dc.contributor.author | Abbott, D.C. | - |
dc.contributor.author | Abud, A.A. | - |
dc.contributor.author | Abeling, K. | - |
dc.contributor.author | Abhayasinghe, D.K. | - |
dc.contributor.author | Abidi, S.H. | - |
dc.date.accessioned | 2024-07-21T18:45:43Z | - |
dc.date.available | 2024-07-21T18:45:43Z | - |
dc.date.issued | 2024 | - |
dc.identifier.issn | 2510-2044 | - |
dc.identifier.uri | https://doi.org/10.1007/s41781-023-00106-9 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/11652 | - |
dc.description.abstract | The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques. © The Author(s) 2024. | en_US |
dc.description.sponsorship | Australian Research Council, ARC; Centre National pour la Recherche Scientifique et Technique, CNRST; Fundação para a Ciência e a Tecnologia, FCT; Narodowe Centrum Nauki, NCN; National Science Foundation, NSF; Science and Technology Facilities Council, STFC; H2020 Marie Skłodowska-Curie Actions, MSCA; Japan Society for the Promotion of Science, JSPS; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO; Ministerio de Ciencia e Innovación, MCIN; Ministry of Science and Technology, Taiwan, MOST; Israel Science Foundation, ISF; Leverhulme Trust; Staatssekretariat für Bildung, Forschung und Innovation, SBFI; Javna Agencija za Raziskovalno Dejavnost RS, ARRS; Engineering Research Centers, ERC; Generalitat de Catalunya; Instituto Nazionale di Fisica Nucleare, INFN; Bundesministerium für Wissenschaft, Forschung und Wirtschaft, BMWFW; Austrian Science Fund, FWF; Narodowa Agencja Wymiany Akademickiej, NAWA; Alabama Space Grant Consortium, ASGC; Agencia Nacional de Investigación y Desarrollo, ANID; Bundesministerium für Bildung und Forschung, BMBF; Canada Foundation for Innovation, CFI; Helmholtz-Gemeinschaft, HGF; Danmarks Grundforskningsfond, DNRF; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Karlsruhe Institute of Technology, KIT; Canarie; Vermont Agency of Natural Resources, ANR; Göran Gustafssons Stiftelser; California Department of Fish and Game, DFG; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MPNTR; U.S. Department of Energy, USDOE; European Cooperation in Science and Technology, COST; National Research Council, NRC; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP; Institutul de Fizică Atomică, IFA; Natural Sciences and Engineering Research Council of Canada, NSERC; Nella and Leon Benoziyo Center for Neurological Diseases, Weizmann Institute of Science; Irish Rugby Football Union, IRFU; Chinese Academy of Sciences, CAS; Defence Science Institute, DSI; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; Compute Canada; Agencia Nacional de Promoción Científica y Tecnológica, ANPCyT; Royal Society; Minerva Foundation; National Research Foundation, NRF; European Regional Development Fund, ERDF; CERN; Ministerstvo Školství, Mládeže a Tělovýchovy, MŠMT; Brookhaven National Laboratory, BNL; Alexander von Humboldt-Stiftung, AvH; Multiple Sclerosis Scientific Research Foundation, MSSRF; Horizon 2020; British Columbia Knowledge Development Fund, BCKDF; Ministry of Education, Culture, Sports, Science and Technology, MEXT; National Natural Science Foundation of China, NSFC; CC-IN2P3; 2014-2021; SCI/013; IN2P3-CNRS; CRC Health Group, CRC: 21/SCI/017; CRC Health Group, CRC | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer Nature | en_US |
dc.relation.ispartof | Computing and Software for Big Science | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.title | Deep Generative Models for Fast Photon Shower Simulation in Atlas | en_US |
dc.type | Article | en_US |
dc.department | TOBB ETÜ | en_US |
dc.identifier.volume | 8 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.scopus | 2-s2.0-85189330049 | en_US |
dc.institutionauthor | … | - |
dc.identifier.doi | 10.1007/s41781-023-00106-9 | - |
dc.authorscopusid | 26326745400 | - |
dc.authorscopusid | 35226946900 | - |
dc.authorscopusid | 57208306618 | - |
dc.authorscopusid | 57208907456 | - |
dc.authorscopusid | 57210132793 | - |
dc.authorscopusid | 57193183451 | - |
dc.authorscopusid | 56536227400 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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