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Jet flavour tagging for the ATLAS Experiment

The ability to identify jets stemming from the hadronisation of b- quarks (b-jets) is crucial for the physics program of ATLAS. The higher pileup conditions and the growing interest for measurements including c-jets and for searches in the high transverse momentum regime make the task more and more...

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Autor principal: Centonze, Martino Salomone
Lenguaje:eng
Publicado: 2021
Materias:
Acceso en línea:http://cds.cern.ch/record/2781293
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author Centonze, Martino Salomone
author_facet Centonze, Martino Salomone
author_sort Centonze, Martino Salomone
collection CERN
description The ability to identify jets stemming from the hadronisation of b- quarks (b-jets) is crucial for the physics program of ATLAS. The higher pileup conditions and the growing interest for measurements including c-jets and for searches in the high transverse momentum regime make the task more and more complex. The algorithms responsible for establishing the jet’s flavour are evolving quickly, exploiting powerful multivariate and deep machine learning techniques. Since the primary input to any such algorithm consists of charged-particle tracks within the jet, the identification of jets from heavy-flavor decays depends strongly on the tracking efficiency and resolution and the robustness of the track-jet association logic. Flavour-tagging techniques in ATLAS will be reviewed, presenting the state-of-the-art in terms of algorithms, with focus on the capability to reconstruct and select the relevant tracks produced in the ATLAS Inner Detector.
id cern-2781293
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2021
record_format invenio
spelling cern-27812932022-11-15T13:38:45Zhttp://cds.cern.ch/record/2781293engCentonze, Martino SalomoneJet flavour tagging for the ATLAS ExperimentParticle Physics - ExperimentThe ability to identify jets stemming from the hadronisation of b- quarks (b-jets) is crucial for the physics program of ATLAS. The higher pileup conditions and the growing interest for measurements including c-jets and for searches in the high transverse momentum regime make the task more and more complex. The algorithms responsible for establishing the jet’s flavour are evolving quickly, exploiting powerful multivariate and deep machine learning techniques. Since the primary input to any such algorithm consists of charged-particle tracks within the jet, the identification of jets from heavy-flavor decays depends strongly on the tracking efficiency and resolution and the robustness of the track-jet association logic. Flavour-tagging techniques in ATLAS will be reviewed, presenting the state-of-the-art in terms of algorithms, with focus on the capability to reconstruct and select the relevant tracks produced in the ATLAS Inner Detector.ATL-PHYS-SLIDE-2021-534oai:cds.cern.ch:27812932021-09-16
spellingShingle Particle Physics - Experiment
Centonze, Martino Salomone
Jet flavour tagging for the ATLAS Experiment
title Jet flavour tagging for the ATLAS Experiment
title_full Jet flavour tagging for the ATLAS Experiment
title_fullStr Jet flavour tagging for the ATLAS Experiment
title_full_unstemmed Jet flavour tagging for the ATLAS Experiment
title_short Jet flavour tagging for the ATLAS Experiment
title_sort jet flavour tagging for the atlas experiment
topic Particle Physics - Experiment
url http://cds.cern.ch/record/2781293
work_keys_str_mv AT centonzemartinosalomone jetflavourtaggingfortheatlasexperiment