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

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dc.WoS.categoriesAstronomy & AstrophysicsPhysics, Particles & Fieldsen_US
dc.authorid0009-0008-7123-3924en_US
dc.contributor.authorÇakır, Affan
dc.date.accessioned2024-04-18T09:09:21Z
dc.date.available2024-04-18T09:09:21Z
dc.date.issued2023-09-05
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.fullTextLevelFull Texten_US
dc.identifier.doi10.1103/PhysRevD.108.052002
dc.identifier.issn2470-0029
dc.identifier.issn2470-0010
dc.identifier.scopus2-s2.0-85175427508en_US
dc.identifier.urihttps://hdl.handle.net/11411/5271
dc.identifier.urihttps://doi.org/10.1103/PhysRevD.108.052002
dc.identifier.wosWOS:001091059400002en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.issue5en_US
dc.language.isoenen_US
dc.nationalInternationalen_US
dc.numberofauthors200+en_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 detector
dc.typeArticle
dc.volume108en_US

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