Using ML techniques to discriminate the tHq(b¯b) decay channel signal from background

Machine Learning techniques are of very importance when analyzing large amounts of data as the ones acquired by the ATLAS detector. This project focus on the employment of Graph Convolutional Networks (GCN) together with the Deep Graph Library (DGL) to discriminate the signal of the production of a...

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Autor principal: Monteiro Fernandes Alves, Arthur
Lenguaje:eng
Publicado: 2022
Materias:
Physics in General
Acceso en línea:http://cds.cern.ch/record/2827606
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author Monteiro Fernandes Alves, Arthur
author_facet Monteiro Fernandes Alves, Arthur
author_sort Monteiro Fernandes Alves, Arthur
collection CERN
description Machine Learning techniques are of very importance when analyzing large amounts of data as the ones acquired by the ATLAS detector. This project focus on the employment of Graph Convolutional Networks (GCN) together with the Deep Graph Library (DGL) to discriminate the signal of the production of a Higgs boson and a single-top quark in the tHq(bb) channel. Python scripts were written to create the graphs and to train and test the Neural Networks. The main goal of using DGL to create the GCN was achieved, however several parameters still have to be altered in order to get a higher test accuracy.
id cern-2827606
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2022
record_format invenio
spelling cern-28276062022-09-21T18:08:48Zhttp://cds.cern.ch/record/2827606engMonteiro Fernandes Alves, ArthurUsing ML techniques to discriminate the tHq(b¯b) decay channel signal from backgroundPhysics in GeneralMachine Learning techniques are of very importance when analyzing large amounts of data as the ones acquired by the ATLAS detector. This project focus on the employment of Graph Convolutional Networks (GCN) together with the Deep Graph Library (DGL) to discriminate the signal of the production of a Higgs boson and a single-top quark in the tHq(bb) channel. Python scripts were written to create the graphs and to train and test the Neural Networks. The main goal of using DGL to create the GCN was achieved, however several parameters still have to be altered in order to get a higher test accuracy.CERN-STUDENTS-Note-2022-162oai:cds.cern.ch:28276062022-09-20
spellingShingle Physics in General
Monteiro Fernandes Alves, Arthur
Using ML techniques to discriminate the tHq(b¯b) decay channel signal from background
title Using ML techniques to discriminate the tHq(b¯b) decay channel signal from background
title_full Using ML techniques to discriminate the tHq(b¯b) decay channel signal from background
title_fullStr Using ML techniques to discriminate the tHq(b¯b) decay channel signal from background
title_full_unstemmed Using ML techniques to discriminate the tHq(b¯b) decay channel signal from background
title_short Using ML techniques to discriminate the tHq(b¯b) decay channel signal from background
title_sort using ml techniques to discriminate the thq(b¯b) decay channel signal from background
topic Physics in General
url http://cds.cern.ch/record/2827606
work_keys_str_mv AT monteirofernandesalvesarthur usingmltechniquestodiscriminatethethqbbdecaychannelsignalfrombackground

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