Improving ATLAS Hadronic Object Performance with ML/AI Algorithms

Experimental uncertainties related to hadronic object reconstruction can limit the precision of physics analyses at the LHC, and so improvements in performance have the potential to broadly increase the impact of results. Hadronic object reconstruction is also one of the most promising settings for...

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Detalles Bibliográficos
Autor principal: Kong, Albert
Lenguaje:eng
Publicado: 2023
Materias:
Particle Physics - Experiment
Acceso en línea:http://cds.cern.ch/record/2867821
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author Kong, Albert
author_facet Kong, Albert
author_sort Kong, Albert
collection CERN
description Experimental uncertainties related to hadronic object reconstruction can limit the precision of physics analyses at the LHC, and so improvements in performance have the potential to broadly increase the impact of results. Hadronic object reconstruction is also one of the most promising settings for cutting-edge machine learning and artificial intelligence algorithms at the LHC. Recent refinements to reconstruction and calibration procedures for ATLAS jets and MET result in reduced uncertainties, improved pileup stability and other performance gains. In this contribution, selected highlights of these developments will be presented.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2023
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spelling cern-28678212023-08-18T19:52:22Zhttp://cds.cern.ch/record/2867821engKong, AlbertImproving ATLAS Hadronic Object Performance with ML/AI AlgorithmsParticle Physics - ExperimentExperimental uncertainties related to hadronic object reconstruction can limit the precision of physics analyses at the LHC, and so improvements in performance have the potential to broadly increase the impact of results. Hadronic object reconstruction is also one of the most promising settings for cutting-edge machine learning and artificial intelligence algorithms at the LHC. Recent refinements to reconstruction and calibration procedures for ATLAS jets and MET result in reduced uncertainties, improved pileup stability and other performance gains. In this contribution, selected highlights of these developments will be presented.ATL-PHYS-SLIDE-2023-342oai:cds.cern.ch:28678212023-08-18
spellingShingle Particle Physics - Experiment
Kong, Albert
Improving ATLAS Hadronic Object Performance with ML/AI Algorithms
title Improving ATLAS Hadronic Object Performance with ML/AI Algorithms
title_full Improving ATLAS Hadronic Object Performance with ML/AI Algorithms
title_fullStr Improving ATLAS Hadronic Object Performance with ML/AI Algorithms
title_full_unstemmed Improving ATLAS Hadronic Object Performance with ML/AI Algorithms
title_short Improving ATLAS Hadronic Object Performance with ML/AI Algorithms
title_sort improving atlas hadronic object performance with ml/ai algorithms
topic Particle Physics - Experiment
url http://cds.cern.ch/record/2867821
work_keys_str_mv AT kongalbert improvingatlashadronicobjectperformancewithmlaialgorithms

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