Machine Learning approach for the search of heavy diboson resonances in semi-leptonic final state at $\sqrt{s}$= 13 TeV with the ATLAS detector

A Recurrent Neural Network-based approach has been adopted for the classification of the production mechanisms in the search of heavy resonances decaying in two bosons. The search is performed using proton-proton collision data recorded with the ATLAS detector from 2015 to 2018. The investigated fin...

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Detalles Bibliográficos
Autor principal: Auricchio, S
Lenguaje:eng
Publicado: 2022
Materias:
Particle Physics - Experiment
Acceso en línea:https://dx.doi.org/10.1393/ncc/i2022-22092-9
http://cds.cern.ch/record/2837861
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author Auricchio, S
author_facet Auricchio, S
author_sort Auricchio, S
collection CERN
description A Recurrent Neural Network-based approach has been adopted for the classification of the production mechanisms in the search of heavy resonances decaying in two bosons. The search is performed using proton-proton collision data recorded with the ATLAS detector from 2015 to 2018. The investigated final state is semi-leptonic, where one boson decays in two leptons and the other decays hadronically. No excesses have been found in data with respect to the background-only hypothesis. Upper bounds on the production cross sections of heavy scalar, vector or tensor resonances are derived in the mass range 300–5000 GeV.
id cern-2837861
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2022
record_format invenio
spelling cern-28378612022-11-17T14:24:58Zdoi:10.1393/ncc/i2022-22092-9http://cds.cern.ch/record/2837861engAuricchio, SMachine Learning approach for the search of heavy diboson resonances in semi-leptonic final state at $\sqrt{s}$= 13 TeV with the ATLAS detectorParticle Physics - ExperimentA Recurrent Neural Network-based approach has been adopted for the classification of the production mechanisms in the search of heavy resonances decaying in two bosons. The search is performed using proton-proton collision data recorded with the ATLAS detector from 2015 to 2018. The investigated final state is semi-leptonic, where one boson decays in two leptons and the other decays hadronically. No excesses have been found in data with respect to the background-only hypothesis. Upper bounds on the production cross sections of heavy scalar, vector or tensor resonances are derived in the mass range 300–5000 GeV.oai:cds.cern.ch:28378612022
spellingShingle Particle Physics - Experiment
Auricchio, S
Machine Learning approach for the search of heavy diboson resonances in semi-leptonic final state at $\sqrt{s}$= 13 TeV with the ATLAS detector
title Machine Learning approach for the search of heavy diboson resonances in semi-leptonic final state at $\sqrt{s}$= 13 TeV with the ATLAS detector
title_full Machine Learning approach for the search of heavy diboson resonances in semi-leptonic final state at $\sqrt{s}$= 13 TeV with the ATLAS detector
title_fullStr Machine Learning approach for the search of heavy diboson resonances in semi-leptonic final state at $\sqrt{s}$= 13 TeV with the ATLAS detector
title_full_unstemmed Machine Learning approach for the search of heavy diboson resonances in semi-leptonic final state at $\sqrt{s}$= 13 TeV with the ATLAS detector
title_short Machine Learning approach for the search of heavy diboson resonances in semi-leptonic final state at $\sqrt{s}$= 13 TeV with the ATLAS detector
title_sort machine learning approach for the search of heavy diboson resonances in semi-leptonic final state at $\sqrt{s}$= 13 tev with the atlas detector
topic Particle Physics - Experiment
url https://dx.doi.org/10.1393/ncc/i2022-22092-9
http://cds.cern.ch/record/2837861
work_keys_str_mv AT auricchios machinelearningapproachforthesearchofheavydibosonresonancesinsemileptonicfinalstateatsqrts13tevwiththeatlasdetector

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