Flavour tagging with graph neural networks with the ATLAS detector

Autor:	Duperrin, Arnaud
Přispěvatelé:	Centre de Physique des Particules de Marseille (CPPM), Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), ATLAS
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	High Energy Physics - Experiment (hep-ex) CERN Lab flavor neural network p p scattering [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex] TeV FOS: Physical sciences CERN LHC Coll upgrade ATLAS performance Particle Physics - Experiment High Energy Physics - Experiment
Zdroj:	30th International Workshop on Deep-Inelastic Scattering and Related Subjects 30th International Workshop on Deep-Inelastic Scattering and Related Subjects, Mar 2023, East Lansing, United States
Popis:	The identification of jets containing a $b$-hadron, referred to as $b$-tagging, plays an important role for various physics measurements and searches carried out by the ATLAS experiment at the CERN Large Hadron Collider (LHC). The most recent $b$-tagging algorithm developments based on graph neural network architectures are presented. Preliminary performance on Run 3 data in $pp$ collisions at $\sqrt s = 13.6$ TeV is shown and expected performance at the High-Luminosity LHC (HL-LHC) discussed. 6 pages, 2 figures, 1 table, Presented at DIS2023: XXX International Workshop on Deep-Inelastic Scattering and Related Subjects, Michigan State University, USA, 27-31 March 2023
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f3a2e8a280c2214b0ce9a456b6d0407e http://arxiv.org/abs/2306.04415 Zobrazit plný text záznamu