Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector

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dc.contributor.authorTumasyan, A.
dc.contributor.authorAdam, W.
dc.contributor.authorAndrejkovic, J. W.
dc.contributor.authorBergauer, T.
dc.contributor.authorChatterjee, S.
dc.contributor.authorDamanakis, K.
dc.contributor.authorThe ATLAS collaboration
dc.date.accessioned2024-03-09T18:48:32Z
dc.date.available2024-03-09T18:48:32Z
dc.date.issued2023
dc.departmentİzmir Bakırçay Üniversitesien_US
dc.description.abstractA novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentz boosted particles. Using an end-to-end deep learning strategy, the technique bypasses existing rule-based particle reconstruction methods typically used in high energy physics analyses. It uses minimally processed detector data as input and directly outputs particle properties of interest. The new technique is demonstrated for the reconstruction of the invariant mass of particles decaying in the CMS detector. The decay of a hypothetical scalar particle A into two photons, A gamma gamma, is chosen as a benchmark decay. Lorentz boosts gamma L 1/4 60-600 are considered, ranging from regimes where both photons are resolved to those where the photons are closely merged as one object. A training method using domain continuation is introduced, enabling the invariant mass reconstruction of unresolved photon pairs in a novel way. The new technique is validated using pi 0 gamma gamma decays in LHC collision data.en_US
dc.description.sponsorshipCouncil of Science and Industrial Research, India [K 133046, K 138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, TKP2021-NKTA-64]; Latvian Council of Science; Ministry of Education and Science [2022/WK/14]; National Science Center; Opus [2021/41/B/ST2/01369, 2021/43/B/ST2/01552, CEECIND/01334/2018]; National Priorities Research Program by Qatar National Research Fund; ERDF a way of making Europe [MCIN/AEI/10.13039/501100011033]; Programa Severo Ochoa del Principado de Asturias (Spain) [MDM-2017-0765]; Chulalongkorn Academic into Its 2nd Century Project Advancement Project; National Science, Research and Innovation Fund via the Program Management Unit for Human Resources AMP; Institutional Development, Research and Innovation [B05F650021]; Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (USA)en_US
dc.description.sponsorshipWe congratulate our colleagues in the CERN acceleratordepartments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: BMBWF and FWF (Austria) ; FNRS and FWO (Belgium) ; CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil) ; MES and BNSF (Bulgaria) ; CERN; CAS, MoST, and NSFC (China) ; MINCIENCIAS (Colombia) ; MSES and CSF (Croatia) ; RIF (Cyprus) ; SENESCYT (Ecuador) ; MoER, ERC PUT and ERDF (Estonia) ; Academy of Finland, MEC, and HIP (Finland) ; CEA and CNRS/IN2P3 (France) ; BMBF, DFG, and HGF (Germany) ; GSRI (Greece) ; NKFIH (Hungary) ; DAE and DST (India) ; IPM (Iran) ; SFI (Ireland) ; INFN (Italy) ; MSIP and NRF (Republic of Korea) ; MES (Latvia) ; LAS (Lithuania) ; MOE and UM (Malaysia) ; BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico) ; MOS (Montenegro) ; MBIE (New Zealand) ; PAEC (Pakistan) ; MES and NSC (Poland) ; FCT (Portugal) ; MESTD (Serbia) ; MCIN/AEI and PCTI (Spain) ; MOSTR (Sri Lanka) ; Swiss Funding Agencies (Switzerland) ; MST (Taipei) ; MHESI and NSTDA (Thailand) ; TUBITAK and TENMAK (Turkey) ; NASU (Ukraine) ; STFC (United Kingdom) ; DOE and NSF (USA) . Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, Contracts No. 675440, No. 724704, No. 752730, No. 758316, No. 765710, No. 824093, No. 884104, and COST Action CA16108 (European Union) ; the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium) ; the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium) ; the F. R. S.-FNRS and FWO (Belgium) under the Excellence of Science- EOS-be.h Project No. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Hellenic Foundation for Research and Innovation (HFRI) , Project No. 2288 (Greece) ; the Deutsche Forschungsgemeinschaft (DFG) , under Germany's Excellence Strategy-EXC 2121 Quantum Universe- 390833306, and under Project No. 400140256- GRK2497; the Hungarian Academy of Sciences, the New National Excellence Program-& Uacute;NKP, the NKFIH Research Grants No. K 124845, No. K 124850, No. K 128713, No. K 128786, No. K 129058, No. K 131991, No. K 133046, No. K 138136, No. K 143460, No. K 143477, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary) ; the Council of Science and Industrial Research, India; the Latvian Council of Science; the Ministry of Education and Science, Project No. 2022/WK/14, and the National Science Center, Contracts No. Opus 2021/41/B/ST2/01369 and No. 2021/43/B/ST2/01552 (Poland) ; the Fundac & atilde;o para a Ciencia e a Tecnologia, Grant No. CEECIND/01334/2018 (Portugal) ; the National Priorities Research Program by Qatar National Research Fund; MCIN/AEI/10.; r 13039/501100011033, ERDF a way of making Europe, and the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, Grant No. MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain) ; the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation, Grant No. B05F650021 (Thailand) ; the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, Contract No. C-1845; and the Weston Havens Foundation (USA) .r No. K 133046, No. K 138136, No. K 143460, No. K 143477, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary) ; the Council of Science and Industrial Research, India; the Latvian Council of Science; the Ministry of Education and Science, Project No. 2022/WK/14, and the National Science Center, Contracts No. Opus 2021/41/B/ST2/01369 and No. 2021/43/B/ST2/01552 (Poland) ; the Fundac & atilde;o para a Ciencia e a Tecnologia, Grant No. CEECIND/01334/2018 (Portugal) ; the National Priorities Research Program by Qatar National Research Fund; MCIN/AEI/10.13039/501100011033, ERDF a way of making Europe, and the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, Grant No. MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain) ; the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation, Grant No. B05F650021 (Thailand) ; the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, Contract No. C-1845; and the Weston Havens Foundation (USA) .en_US
dc.identifier.doi10.1103/PhysRevD.108.052002
dc.identifier.issn2470-0010
dc.identifier.issn2470-0029
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85175427508en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1103/PhysRevD.108.052002
dc.identifier.urihttps://hdl.handle.net/20.500.14034/1370
dc.identifier.volume108en_US
dc.identifier.wosWOS:001091059400002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAmer Physical Socen_US
dc.relation.ispartofPhysical Review Den_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleReconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detectoren_US
dc.typeArticleen_US

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