Axion-Like-Particle Search Using Machine Learning for the Signal Sensitivity Optimization with Run-2 LHC Data Recorded by the ATLAS Experiment

The neutral Standard Model Higgs bo- son was discovered in 2012 at CERN, and the search for further particles of extended models continues. In particular, the search for an Axion-Like-Particle (ALP). Using machine learning technologies, this analysis addresses the separation of ALP production from u...

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Autor principal: Matousek, Ondrej
Lenguaje:eng
Publicado: 2023
Materias:
Particle Physics - Experiment
Detectors and Experimental Techniques
Acceso en línea:http://cds.cern.ch/record/2862249
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author Matousek, Ondrej
author_facet Matousek, Ondrej
author_sort Matousek, Ondrej
collection CERN
description The neutral Standard Model Higgs bo- son was discovered in 2012 at CERN, and the search for further particles of extended models continues. In particular, the search for an Axion-Like-Particle (ALP). Using machine learning technologies, this analysis addresses the separation of ALP production from unwanted background reactions. In this project, the Run-2 data from the ATLAS detector are used and the efficiency as well as the significance of the machine learning algorithm is optimized as a function of theoretical ALP mass.
id cern-2862249
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2023
record_format invenio
spelling cern-28622492023-07-27T14:21:43Zhttp://cds.cern.ch/record/2862249engMatousek, OndrejAxion-Like-Particle Search Using Machine Learning for the Signal Sensitivity Optimization with Run-2 LHC Data Recorded by the ATLAS ExperimentParticle Physics - ExperimentDetectors and Experimental TechniquesThe neutral Standard Model Higgs bo- son was discovered in 2012 at CERN, and the search for further particles of extended models continues. In particular, the search for an Axion-Like-Particle (ALP). Using machine learning technologies, this analysis addresses the separation of ALP production from unwanted background reactions. In this project, the Run-2 data from the ATLAS detector are used and the efficiency as well as the significance of the machine learning algorithm is optimized as a function of theoretical ALP mass.CERN-THESIS-2023-075oai:cds.cern.ch:28622492023-06-18T13:39:26Z
spellingShingle Particle Physics - Experiment
Detectors and Experimental Techniques
Matousek, Ondrej
Axion-Like-Particle Search Using Machine Learning for the Signal Sensitivity Optimization with Run-2 LHC Data Recorded by the ATLAS Experiment
title Axion-Like-Particle Search Using Machine Learning for the Signal Sensitivity Optimization with Run-2 LHC Data Recorded by the ATLAS Experiment
title_full Axion-Like-Particle Search Using Machine Learning for the Signal Sensitivity Optimization with Run-2 LHC Data Recorded by the ATLAS Experiment
title_fullStr Axion-Like-Particle Search Using Machine Learning for the Signal Sensitivity Optimization with Run-2 LHC Data Recorded by the ATLAS Experiment
title_full_unstemmed Axion-Like-Particle Search Using Machine Learning for the Signal Sensitivity Optimization with Run-2 LHC Data Recorded by the ATLAS Experiment
title_short Axion-Like-Particle Search Using Machine Learning for the Signal Sensitivity Optimization with Run-2 LHC Data Recorded by the ATLAS Experiment
title_sort axion-like-particle search using machine learning for the signal sensitivity optimization with run-2 lhc data recorded by the atlas experiment
topic Particle Physics - Experiment
Detectors and Experimental Techniques
url http://cds.cern.ch/record/2862249
work_keys_str_mv AT matousekondrej axionlikeparticlesearchusingmachinelearningforthesignalsensitivityoptimizationwithrun2lhcdatarecordedbytheatlasexperiment

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