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Электронный каталог: Aad, G. - ATLAS Flavour-Tagging Algorithms for the LHC Run 2 pp Collision Dataset
Aad, G. - ATLAS Flavour-Tagging Algorithms for the LHC Run 2 pp Collision Dataset
Статья
Автор: Aad, G.
The European Physical Journal C [Electronic resource]: ATLAS Flavour-Tagging Algorithms for the LHC Run 2 pp Collision Dataset
б.г.
ISBN отсутствует
Автор: Aad, G.
The European Physical Journal C [Electronic resource]: ATLAS Flavour-Tagging Algorithms for the LHC Run 2 pp Collision Dataset
б.г.
ISBN отсутствует
Статья
Aad, G.
ATLAS Flavour-Tagging Algorithms for the LHC Run 2 pp Collision Dataset / G.Aad, F.Ahmadov, I.N.Aleksandrov, V.A.Bednyakov, I.R.Boyko, I.A.Budagov, G.A.Chelkov, A.Cheplakov, M.V.Chizhov, D.V.Dedovich, M.Demichev, N.Huseynov, A.Gongadze, M.I.Gostkin, S.N.Karpov, Z.M.Karpova, U.Kruchonak, V.Kukhtin, Y.Kulchitsky, E.Ladygin, V.Lyubushkin, T.Lyubushkina, S.Malyukov, M.Mineev, E.Plotnikova, I.N.Potrap, N.A.Rusakovich, M.Shiyakova, A.Soloshenko, S.Turchikhin, T.Turtuvshin, I.Yeletskikh, A.Zhemchugov, N.I.Zimine, [ATLAS Collab.] // The European Physical Journal C [Electronic resource]. – 2023. – Vol.83, No.7. – P.681. – URL: https://doi.org/10.1140/epjc/s10052-023-11699-1. – Bibliogr.:57.
The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset of TeV pp collisions from Run 2 of the Large Hadron Collider are presented. These new tagging algorithms are based on recurrent and deep neural networks, and their performance is evaluated in simulated collision events. These developments yield considerable improvements over previous jet-flavour identification strategies. At the 77% b-jet identification efficiency operating point, light-jet (charm-jet) rejection factors of 170 (5) are achieved in a sample of simulated Standard Model events; similarly, at a c-jet identification efficiency of 30%, a light-jet (b-jet) rejection factor of 70 (9) is obtained.
ОИЯИ = ОИЯИ (JINR)2023
Спец.(статьи,препринты) = С 346.2в - Взаимодействие протонов с протонами
Aad, G.
ATLAS Flavour-Tagging Algorithms for the LHC Run 2 pp Collision Dataset / G.Aad, F.Ahmadov, I.N.Aleksandrov, V.A.Bednyakov, I.R.Boyko, I.A.Budagov, G.A.Chelkov, A.Cheplakov, M.V.Chizhov, D.V.Dedovich, M.Demichev, N.Huseynov, A.Gongadze, M.I.Gostkin, S.N.Karpov, Z.M.Karpova, U.Kruchonak, V.Kukhtin, Y.Kulchitsky, E.Ladygin, V.Lyubushkin, T.Lyubushkina, S.Malyukov, M.Mineev, E.Plotnikova, I.N.Potrap, N.A.Rusakovich, M.Shiyakova, A.Soloshenko, S.Turchikhin, T.Turtuvshin, I.Yeletskikh, A.Zhemchugov, N.I.Zimine, [ATLAS Collab.] // The European Physical Journal C [Electronic resource]. – 2023. – Vol.83, No.7. – P.681. – URL: https://doi.org/10.1140/epjc/s10052-023-11699-1. – Bibliogr.:57.
The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset of TeV pp collisions from Run 2 of the Large Hadron Collider are presented. These new tagging algorithms are based on recurrent and deep neural networks, and their performance is evaluated in simulated collision events. These developments yield considerable improvements over previous jet-flavour identification strategies. At the 77% b-jet identification efficiency operating point, light-jet (charm-jet) rejection factors of 170 (5) are achieved in a sample of simulated Standard Model events; similarly, at a c-jet identification efficiency of 30%, a light-jet (b-jet) rejection factor of 70 (9) is obtained.
ОИЯИ = ОИЯИ (JINR)2023
Спец.(статьи,препринты) = С 346.2в - Взаимодействие протонов с протонами