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dc.contributor.authorHerrera Maldonado, Luis Javier 
dc.contributor.authorBaños Legrán, Oresti 
dc.contributor.authorCarceller López, Juan Miguel
dc.contributor.authorCarrillo Pérez, Francisco
dc.contributor.authorGuillén Perales, Alberto 
dc.date.accessioned2020-11-30T07:36:30Z
dc.date.available2020-11-30T07:36:30Z
dc.date.issued2020
dc.identifier.citationHerrera, L.J.; Todero Peixoto, C.J.; Baños, O.; Carceller, J.M.; Carrillo, F.; Guillén, A. Composition Classification of Ultra-High Energy Cosmic Rays. Entropy 2020, 22, 998. [DOI: https://doi.org/10.3390/e22090998]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/64536
dc.description.abstractThe study of cosmic rays remains as one of the most challenging research fields in Physics. From the many questions still open in this area, knowledge of the type of primary for each event remains as one of the most important issues. All of the cosmic rays observatories have been trying to solve this question for at least six decades, but have not yet succeeded. The main obstacle is the impossibility of directly detecting high energy primary events, being necessary to use Monte Carlo models and simulations to characterize generated particles cascades. This work presents the results attained using a simulated dataset that was provided by the Monte Carlo code CORSIKA, which is a simulator of high energy particles interactions with the atmosphere, resulting in a cascade of secondary particles extending for a few kilometers (in diameter) at ground level. Using this simulated data, a set of machine learning classifiers have been designed and trained, and their computational cost and effectiveness compared, when classifying the type of primary under ideal measuring conditions. Additionally, a feature selection algorithm has allowed for identifying the relevance of the considered features. The results confirm the importance of the electromagnetic-muonic component separation from signal data measured for the problem. The obtained results are quite encouraging and open new work lines for future more restrictive simulations.es_ES
dc.description.sponsorshipSpanish Ministry of Science, Innovation and Universities FPA2017-85197-P RTI2018-101674-B-I00es_ES
dc.description.sponsorshipEuropean Union (EU)es_ES
dc.description.sponsorshipCENAPAD-SP (Centro Nacional de Processamento de Alto Desempenho em Sao Paulo) UNICAMP/FINEP - MCTes_ES
dc.description.sponsorshipFundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)es_ES
dc.description.sponsorshipNational Council for Scientific and Technological Development (CNPq) 2016/19764-9es_ES
dc.description.sponsorship404993/2016-8es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectCosmic rays es_ES
dc.subjectUltra-high energyes_ES
dc.subjectMass compositiones_ES
dc.subjectFeature selectiones_ES
dc.subjectDeep learninges_ES
dc.titleComposition Classification of Ultra-High Energy Cosmic Rayses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3390/e22090998


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España