Machine Learning Algorithms for $b$-Jet Tagging at the ATLAS Experiment

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The separation of $b$-quark initiated jets from those coming from lighter quark flavors ($b$-tagging) is a fundamental tool for the ATLAS physics program at the CERN Large Hadron Collider. The most powerful $b$-tagging algorithms combine information from low-level taggers, exploiting reconstructed t...

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Autor principal: Paganini, Michela
Lenguaje:eng
Publicado: 2017
Materias:
Particle Physics - Experiment
cs.LG
Computing and Computers
hep-ex
Acceso en línea:https://dx.doi.org/10.1088/1742-6596/1085/4/042031
http://cds.cern.ch/record/2289214
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author Paganini, Michela
author_facet Paganini, Michela
author_sort Paganini, Michela
collection CERN
description The separation of $b$-quark initiated jets from those coming from lighter quark flavors ($b$-tagging) is a fundamental tool for the ATLAS physics program at the CERN Large Hadron Collider. The most powerful $b$-tagging algorithms combine information from low-level taggers, exploiting reconstructed track and vertex information, into machine learning classifiers. The potential of modern deep learning techniques is explored using simulated events, and compared to that achievable from more traditional classifiers such as boosted decision trees.
id cern-2289214
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2017
record_format invenio
spelling cern-22892142021-05-03T20:15:13Zdoi:10.1088/1742-6596/1085/4/042031http://cds.cern.ch/record/2289214engPaganini, MichelaMachine Learning Algorithms for $b$-Jet Tagging at the ATLAS ExperimentParticle Physics - Experimentcs.LGComputing and Computershep-exThe separation of $b$-quark initiated jets from those coming from lighter quark flavors ($b$-tagging) is a fundamental tool for the ATLAS physics program at the CERN Large Hadron Collider. The most powerful $b$-tagging algorithms combine information from low-level taggers, exploiting reconstructed track and vertex information, into machine learning classifiers. The potential of modern deep learning techniques is explored using simulated events, and compared to that achievable from more traditional classifiers such as boosted decision trees.The separation of b-quark initiated jets from those coming from lighter quark flavors (b-tagging) is a fundamental tool for the ATLAS physics program at the CERN Large Hadron Collider. The most powerful b-tagging algorithms combine information from low-level taggers, exploiting reconstructed track and vertex information, into machine learning classifiers. The potential of modern deep learning techniques is explored using simulated events, and compared to that achievable from more traditional classifiers such as boosted decision trees.ATL-PHYS-PROC-2017-211arXiv:1711.08811oai:cds.cern.ch:22892142017-10-15
spellingShingle Particle Physics - Experiment
cs.LG
Computing and Computers
hep-ex
Paganini, Michela
Machine Learning Algorithms for $b$-Jet Tagging at the ATLAS Experiment
title Machine Learning Algorithms for $b$-Jet Tagging at the ATLAS Experiment
title_full Machine Learning Algorithms for $b$-Jet Tagging at the ATLAS Experiment
title_fullStr Machine Learning Algorithms for $b$-Jet Tagging at the ATLAS Experiment
title_full_unstemmed Machine Learning Algorithms for $b$-Jet Tagging at the ATLAS Experiment
title_short Machine Learning Algorithms for $b$-Jet Tagging at the ATLAS Experiment
title_sort machine learning algorithms for $b$-jet tagging at the atlas experiment
topic Particle Physics - Experiment
cs.LG
Computing and Computers
hep-ex
url https://dx.doi.org/10.1088/1742-6596/1085/4/042031
http://cds.cern.ch/record/2289214
work_keys_str_mv AT paganinimichela machinelearningalgorithmsforbjettaggingattheatlasexperiment

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