Auricchio, S. (2022) Machine Learning approach for the search of heavy diboson resonances in semi-leptonic final state at √s = 13 TeV with the ATLAS detector. Il nuovo cimento C, 45 (5). pp. 1-4. ISSN 1826-9885
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Abstract
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.
Item Type: | Article |
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Subjects: | 500 Scienze naturali e Matematica > 530 Fisica |
Depositing User: | Marina Spanti |
Date Deposited: | 06 Sep 2022 12:37 |
Last Modified: | 06 Sep 2022 12:37 |
URI: | http://eprints.bice.rm.cnr.it/id/eprint/22002 |
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