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INVESTIGATING THE VIOLATION OF CHARGE-PARITY SYMMETRY THROUGH TOP-QUARK CHROMOELECTRIC DIPOLE MOMENTS BY USING MACHINE LEARNING TECHNIQUES

dc.contributor.authorIsildak, Bora
dc.contributor.authorHudaverdi, Murat
dc.contributor.authorIlgin, Fatih
dc.contributor.authorHayreter, Alper
dc.contributor.authorSalva, Sinem
dc.contributor.authorSimsek, Ebru
dc.contributor.authorGuyer, Sinan
dc.date.accessioned2026-06-27T14:55:21Z
dc.date.issued2023
dc.description.abstractThere are a number of studies in the literature on the search for Charge -Parity (CP) violating signals in top-quark productions at the LHC. In most of these studies, ChromoMagnetic Dipole Moments (CMDM) and Chro-moElectric Dipole Moments (CEDM) of top quarks are bounded either by deviations from the Standard Model (SM) cross sections or by T-odd asymmetries in di-muon channels. However, the required precision on these cross section values is far beyond from that ATLAS or CMS experiments can reach. In this letter, the investigation of CEDM-based asymmetries in the semileptonic top-pair decays is presented as T-odd asymmetries in the CMS experiment. Expected asymmetry values are determined at the detector level using MadGraph5, Pythia 8, and Delphes softwares along with the discrimination of the signal and the background with Deep Neural Net-works (DNN).en
dc.description.sponsorshipSCOAP3
dc.description.sponsorshipScientific and Technological Research Council of Turkey(TUBITAK) [119F015]
dc.description.urihttps://doi.org/10.5506/aphyspolb.54.5-a4
dc.identifier.doi10.5506/aphyspolb.54.5-a4
dc.identifier.eissn1509-5770
dc.identifier.issn0587-4254
dc.identifier.issue5
dc.identifier.urihttps://hdl.handle.net/20.500.14981/66249
dc.identifier.volume54
dc.identifier.wos001021305200001
dc.language.isoeng
dc.publisherJAGIELLONIAN UNIV PRESS
dc.relation.ispartofACTA PHYSICA POLONICA B
dc.rightsopenAccess
dc.subjectCP-VIOLATION
dc.subjectEFFECTIVE COUPLINGS
dc.subjectPhysics
dc.titleINVESTIGATING THE VIOLATION OF CHARGE-PARITY SYMMETRY THROUGH TOP-QUARK CHROMOELECTRIC DIPOLE MOMENTS BY USING MACHINE LEARNING TECHNIQUES
dc.typeArticle
dspace.entity.typePublication
local.import.sourceWOS

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