Flavour tagging with graph neural networks with the ATLAS detector

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...

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Detalles Bibliográficos
Autor principal: Duperrin, Arnaud
Lenguaje:eng
Publicado: 2023
Materias:
Particle Physics - Experiment
Acceso en línea:http://cds.cern.ch/record/2860610
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author Duperrin, Arnaud
author_facet Duperrin, Arnaud
author_sort Duperrin, Arnaud
collection CERN
description 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.
id cern-2860610
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2023
record_format invenio
spelling cern-28606102023-09-27T08:04:58Zhttp://cds.cern.ch/record/2860610engDuperrin, ArnaudFlavour tagging with graph neural networks with the ATLAS detectorParticle Physics - ExperimentThe 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.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.arXiv:2306.04415ATL-PHYS-PROC-2023-017oai:cds.cern.ch:28606102023-06-01
spellingShingle Particle Physics - Experiment
Duperrin, Arnaud
Flavour tagging with graph neural networks with the ATLAS detector
title Flavour tagging with graph neural networks with the ATLAS detector
title_full Flavour tagging with graph neural networks with the ATLAS detector
title_fullStr Flavour tagging with graph neural networks with the ATLAS detector
title_full_unstemmed Flavour tagging with graph neural networks with the ATLAS detector
title_short Flavour tagging with graph neural networks with the ATLAS detector
title_sort flavour tagging with graph neural networks with the atlas detector
topic Particle Physics - Experiment
url http://cds.cern.ch/record/2860610
work_keys_str_mv AT duperrinarnaud flavourtaggingwithgraphneuralnetworkswiththeatlasdetector

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