Comparative performance of ATLAS boosted 
W taggers using different AI/ML algorithms

Identifying boosted hadronic decays of W/Z bosons is central to many LHC physics analyses. This poster presents the performance of constituent-based W/Z boson taggers using large-radius boosted jets reconstructed from Unified Flow Objects (UFOs) in simulated collisions at sqrt(s)=13 TeV. Several tag...

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Detalles Bibliográficos
Autor principal: LeBlanc, Matt
Lenguaje:eng
Publicado: 2023
Materias:
Particle Physics - Experiment
Acceso en línea:http://cds.cern.ch/record/2866693
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author LeBlanc, Matt
author_facet LeBlanc, Matt
author_sort LeBlanc, Matt
collection CERN
description Identifying boosted hadronic decays of W/Z bosons is central to many LHC physics analyses. This poster presents the performance of constituent-based W/Z boson taggers using large-radius boosted jets reconstructed from Unified Flow Objects (UFOs) in simulated collisions at sqrt(s)=13 TeV. Several taggers which consider all the information contained in the kinematics of the jet constituents are studied. A comparison between these taggers and the current generation of ATLAS W/Z taggers is also provided. Several constituent based taggers are found to improve performance across the wide kinematic range of interest. The dependence of each tagger’s performance on physics modeling is also studied.
id cern-2866693
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2023
record_format invenio
spelling cern-28666932023-08-03T20:42:17Zhttp://cds.cern.ch/record/2866693engLeBlanc, MattComparative performance of ATLAS boosted 
W taggers using different AI/ML algorithmsParticle Physics - ExperimentIdentifying boosted hadronic decays of W/Z bosons is central to many LHC physics analyses. This poster presents the performance of constituent-based W/Z boson taggers using large-radius boosted jets reconstructed from Unified Flow Objects (UFOs) in simulated collisions at sqrt(s)=13 TeV. Several taggers which consider all the information contained in the kinematics of the jet constituents are studied. A comparison between these taggers and the current generation of ATLAS W/Z taggers is also provided. Several constituent based taggers are found to improve performance across the wide kinematic range of interest. The dependence of each tagger’s performance on physics modeling is also studied.ATL-PHYS-SLIDE-2023-319oai:cds.cern.ch:28666932023-08-03
spellingShingle Particle Physics - Experiment
LeBlanc, Matt
Comparative performance of ATLAS boosted 
W taggers using different AI/ML algorithms
title Comparative performance of ATLAS boosted 
W taggers using different AI/ML algorithms
title_full Comparative performance of ATLAS boosted 
W taggers using different AI/ML algorithms
title_fullStr Comparative performance of ATLAS boosted 
W taggers using different AI/ML algorithms
title_full_unstemmed Comparative performance of ATLAS boosted 
W taggers using different AI/ML algorithms
title_short Comparative performance of ATLAS boosted 
W taggers using different AI/ML algorithms
title_sort comparative performance of atlas boosted 
w taggers using different ai/ml algorithms
topic Particle Physics - Experiment
url http://cds.cern.ch/record/2866693
work_keys_str_mv AT leblancmatt comparativeperformanceofatlasboostedwtaggersusingdifferentaimlalgorithms

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