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Automatic event recognition for Higgs boson detection
Several groups of researches have tried, and are still trying, to improve Higgs boson detection. Machine Learning (ML) appears as one of the most promising ways, namely Boosted Decision Trees (BDT), Shallow Neural Networks (SNN), and Deep Neural Networks (DNN). The great advantage of such classifier...
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Lenguaje: | eng |
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2020
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Acceso en línea: | http://cds.cern.ch/record/2722145 |
_version_ | 1780965885109862400 |
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author | Maly, Jakub |
author_facet | Maly, Jakub |
author_sort | Maly, Jakub |
collection | CERN |
description | Several groups of researches have tried, and are still trying, to improve Higgs boson detection. Machine Learning (ML) appears as one of the most promising ways, namely Boosted Decision Trees (BDT), Shallow Neural Networks (SNN), and Deep Neural Networks (DNN). The great advantage of such classifiers is that they can be trained once and then reused several times without needing any significant computational power. Also, they can be free of knowing the physical background, or the meaning of the features. The main aim of this project is to test recently developed ML libraries on data provided by CERN. |
id | cern-2722145 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2020 |
record_format | invenio |
spelling | cern-27221452020-08-20T15:47:13Zhttp://cds.cern.ch/record/2722145engMaly, JakubAutomatic event recognition for Higgs boson detectionParticle Physics - ExperimentSeveral groups of researches have tried, and are still trying, to improve Higgs boson detection. Machine Learning (ML) appears as one of the most promising ways, namely Boosted Decision Trees (BDT), Shallow Neural Networks (SNN), and Deep Neural Networks (DNN). The great advantage of such classifiers is that they can be trained once and then reused several times without needing any significant computational power. Also, they can be free of knowing the physical background, or the meaning of the features. The main aim of this project is to test recently developed ML libraries on data provided by CERN.CERN-THESIS-2020-054oai:cds.cern.ch:27221452020-06-28T19:59:45Z |
spellingShingle | Particle Physics - Experiment Maly, Jakub Automatic event recognition for Higgs boson detection |
title | Automatic event recognition for Higgs boson detection |
title_full | Automatic event recognition for Higgs boson detection |
title_fullStr | Automatic event recognition for Higgs boson detection |
title_full_unstemmed | Automatic event recognition for Higgs boson detection |
title_short | Automatic event recognition for Higgs boson detection |
title_sort | automatic event recognition for higgs boson detection |
topic | Particle Physics - Experiment |
url | http://cds.cern.ch/record/2722145 |
work_keys_str_mv | AT malyjakub automaticeventrecognitionforhiggsbosondetection |