Improving ATLAS Hadronic Object Performance with ML/AI Algorithms

The talk focuses on the use of Machine Learning algorithms in jet reconstruction, calibration and tagging. The most updated results will be shown.

Detalles Bibliográficos
Autor principal: Cirotto, Francesco
Lenguaje:eng
Publicado: 2023
Materias:
Particle Physics - Experiment
Acceso en línea:http://cds.cern.ch/record/2865612
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author Cirotto, Francesco
author_facet Cirotto, Francesco
author_sort Cirotto, Francesco
collection CERN
description The talk focuses on the use of Machine Learning algorithms in jet reconstruction, calibration and tagging. The most updated results will be shown.
id cern-2865612
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2023
record_format invenio
spelling cern-28656122023-07-20T20:41:42Zhttp://cds.cern.ch/record/2865612engCirotto, FrancescoImproving ATLAS Hadronic Object Performance with ML/AI AlgorithmsParticle Physics - ExperimentThe talk focuses on the use of Machine Learning algorithms in jet reconstruction, calibration and tagging. The most updated results will be shown.ATL-PHYS-SLIDE-2023-299oai:cds.cern.ch:28656122023-07-20
spellingShingle Particle Physics - Experiment
Cirotto, Francesco
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/2865612
work_keys_str_mv AT cirottofrancesco improvingatlashadronicobjectperformancewithmlaialgorithms

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