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Deep learning searches for vector-like leptons at the LHC and electron/muon colliders

2023Journal article Open access
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dc.contributor.authorMorais, António P.
dc.contributor.authorOnofre, António
dc.contributor.authorFreitas, Felipe F.
dc.contributor.authorGonçalves, João
dc.contributor.authorPasechnik, Roman
dc.contributor.authorSantos, Rui
dc.date.accessioned2024-03-26T18:33:41Z
dc.date.available2024-03-26T18:33:41Z
dc.date.issued2023
dc.description.abstracthe discovery potential of both singlet and doublet vector-like leptons (VLLs) at the Large Hadron Collider (LHC) as well as at the not-so-far future muon and electron machines is explored. The focus is on a single production channel for LHC direct searches while double production signatures are proposed for the leptonic colliders. A Deep Learning algorithm to determine the discovery (or exclusion) statistical significance at the LHC is employed. While doublet VLLs can be probed up to masses of 1 TeV, their singlet counterparts have very low cross sections and can hardly be tested beyond a few hundreds of GeV at the LHC. This motivates a physics-case analysis in the context of leptonic colliders where one obtains larger cross sections in VLL double production channels, allowing to probe higher mass regimes otherwise inaccessible even to the LHC high-luminosity upgrade.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMorais, A.P., Onofre, A., Freitas, F.F. et al. Deep learning searches for vector-like leptons at the LHC and electron/muon colliders. Eur. Phys. J. C 83, 232 (2023). https://doi.org/10.1140/epjc/s10052-023-11314-3pt_PT
dc.identifier.doi10.1140/epjc/s10052-023-11314-3pt_PT
dc.identifier.issn1434-6044
dc.identifier.urihttp://hdl.handle.net/10400.21/17222
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relationCERN/FISPAR /0002/2017pt_PT
dc.relationCenter for Research and Development in Mathematics and Applications
dc.relationCenter for Research and Development in Mathematics and Applications
dc.relationFrom Higgs Phenomenology to the Unification of Fundamental Interactions
dc.relationCollider and gravitational echoes of Full Unification with deep-learning
dc.relationCollider, gravitational and dark echoes of Grand Unification with Deep Learning
dc.relationCenter for Theoretical and Computational Physics
dc.relationCenter for Theoretical and Computational Physics
dc.relationStandard Model Extensions at the LHC
dc.relationPhenomenological Studies @ the LHC on Top Quark and Higgs Physics
dc.relation.publisherversionhttps://link.springer.com/content/pdf/10.1140/epjc/s10052-023-11314-3.pdfpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectDeep learningpt_PT
dc.subjectLarge Hadron Colliderpt_PT
dc.titleDeep learning searches for vector-like leptons at the LHC and electron/muon colliderspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCenter for Research and Development in Mathematics and Applications
oaire.awardTitleCenter for Research and Development in Mathematics and Applications
oaire.awardTitleFrom Higgs Phenomenology to the Unification of Fundamental Interactions
oaire.awardTitleCollider and gravitational echoes of Full Unification with deep-learning
oaire.awardTitleCollider, gravitational and dark echoes of Grand Unification with Deep Learning
oaire.awardTitleCenter for Theoretical and Computational Physics
oaire.awardTitleCenter for Theoretical and Computational Physics
oaire.awardTitleStandard Model Extensions at the LHC
oaire.awardTitlePhenomenological Studies @ the LHC on Top Quark and Higgs Physics
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oaire.citation.endPage24pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleThe European Physical Journal Cpt_PT
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rcaap.typearticlept_PT
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