Data Analysis of Particle Physics Experiments Based on Machine Learning and the Mitchell’s Criteria

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Commonly the searching and identification of new particles, requires to reach highest efficiencies and purities as well. It demands to apply a chain of cuts that reject the background substantially. In most cases the processes to extract signal from the background is carried out by hand with some as...

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Detalles Bibliográficos
Autor: Nieto-Chaupis, Huber
Formato: artículo
Fecha de Publicación:2020
Institución:Universidad Autónoma del Perú
Repositorio:AUTONOMA-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.autonoma.edu.pe:20.500.13067/1714
Enlace del recurso:https://hdl.handle.net/20.500.13067/1714
https://doi.org/10.1007/978-3-030-46785-2_29
Nivel de acceso:acceso restringido
Materia:Data analysis
Particle Physics Experiments
Machine learning
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dc.title.es_PE.fl_str_mv Data Analysis of Particle Physics Experiments Based on Machine Learning and the Mitchell’s Criteria
title Data Analysis of Particle Physics Experiments Based on Machine Learning and the Mitchell’s Criteria
spellingShingle Data Analysis of Particle Physics Experiments Based on Machine Learning and the Mitchell’s Criteria
Nieto-Chaupis, Huber
Data analysis
Particle Physics Experiments
Machine learning
https://purl.org/pe-repo/ocde/ford#2.02.04
title_short Data Analysis of Particle Physics Experiments Based on Machine Learning and the Mitchell’s Criteria
title_full Data Analysis of Particle Physics Experiments Based on Machine Learning and the Mitchell’s Criteria
title_fullStr Data Analysis of Particle Physics Experiments Based on Machine Learning and the Mitchell’s Criteria
title_full_unstemmed Data Analysis of Particle Physics Experiments Based on Machine Learning and the Mitchell’s Criteria
title_sort Data Analysis of Particle Physics Experiments Based on Machine Learning and the Mitchell’s Criteria
author Nieto-Chaupis, Huber
author_facet Nieto-Chaupis, Huber
author_role author
dc.contributor.author.fl_str_mv Nieto-Chaupis, Huber
dc.subject.es_PE.fl_str_mv Data analysis
Particle Physics Experiments
Machine learning
topic Data analysis
Particle Physics Experiments
Machine learning
https://purl.org/pe-repo/ocde/ford#2.02.04
dc.subject.ocde.es_PE.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.02.04
description Commonly the searching and identification of new particles, requires to reach highest efficiencies and purities as well. It demands to apply a chain of cuts that reject the background substantially. In most cases the processes to extract signal from the background is carried out by hand with some assistance of well designed and intelligent codes that save time and resources in high energy physics experiments. In this paper we present one application of the Mitchell’s criteria to extract efficiently beyond Standard Model signal events yielding an error of order of 1.22%. The usage of Machine Learning schemes appears to be advantageous when large volumes of data need to be scrutinized.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2022-03-03T17:59:12Z
dc.date.available.none.fl_str_mv 2022-03-03T17:59:12Z
dc.date.issued.fl_str_mv 2020
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format article
dc.identifier.citation.es_PE.fl_str_mv Nieto-Chaupis, H. (2019, December). Data Analysis of Particle Physics Experiments Based on Machine Learning and the Mitchell’s Criteria. In International Conference on Smart Technologies, Systems and Applications (pp. 364-374). Springer, Cham.
dc.identifier.isbn.none.fl_str_mv 978-3-030-46785-2
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.13067/1714
dc.identifier.journal.es_PE.fl_str_mv Communications in Computer and Information Science
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1007/978-3-030-46785-2_29
identifier_str_mv Nieto-Chaupis, H. (2019, December). Data Analysis of Particle Physics Experiments Based on Machine Learning and the Mitchell’s Criteria. In International Conference on Smart Technologies, Systems and Applications (pp. 364-374). Springer, Cham.
978-3-030-46785-2
Communications in Computer and Information Science
url https://hdl.handle.net/20.500.13067/1714
https://doi.org/10.1007/978-3-030-46785-2_29
dc.language.iso.es_PE.fl_str_mv eng
language eng
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Data Analysis of Particle Physics Experiments Based on Machine Learning and the Mitchell’s Criteria
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