Deep generative models for fast photon shower simulation in ATLAS

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dc.authorscopusidAndrew John Beddall / 57215802986
dc.authorscopusidSerkant Ali Çetin / 34567544400
dc.authorscopusidSertaç Öztürk / 56421488400
dc.authorscopusidSinem Şimşek / 57210344387
dc.authorwosidAndrew John Beddall / AAE-5820-2022
dc.authorwosidSertaç Öztürk / AGO-2476-2022
dc.authorwosidSerkant Ali Çetin / AGF-0147-2022
dc.authorwosidSinem Şimşek / AGG-2640-2022
dc.contributor.authorAad, G.
dc.contributor.authorAbbott, B.
dc.contributor.authorBeddall, Andrew John
dc.contributor.authorÇetin, Serkant Ali
dc.contributor.authorÖztürk, Sertaç
dc.contributor.authorŞimşek, Sinem
dc.date.accessioned2025-04-18T07:05:55Z
dc.date.available2025-04-18T07:05:55Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Rektörlük, Temel Bilimler Bölümü
dc.description.abstractThe 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.
dc.identifier.citationATLAS collaboration. (2022). Deep generative models for fast photon shower simulation in ATLAS. arXiv preprint arXiv:2210.06204.
dc.identifier.doi10.1007/s41781-023-00106-9
dc.identifier.endpage40
dc.identifier.issn25102044
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85189330049
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1007/s41781-023-00106-9
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6417
dc.identifier.volume8
dc.indekslendigikaynakScopus
dc.institutionauthorBeddall, Andrew John
dc.institutionauthorÇetin, Serkant Ali
dc.institutionauthorÖztürk, Sertaç
dc.institutionauthorŞimşek, Sinem
dc.institutionauthoridAndrew John Beddall / 0000-0002-8451-9672
dc.institutionauthoridSerkant Ali Çetin / 0000-0001-5050-8441
dc.institutionauthoridSertaç Öztürk / 0000-0001-6533-6144
dc.institutionauthoridSinem Şimşek / 0000-0002-9650-3846
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofComputing and software for big science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleDeep generative models for fast photon shower simulation in ATLAS
dc.typeArticle

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