Particle classification in the LAGO water Cherenkov detectors using clustering algorithms

Autores
Torres Peralta, Ticiano Jorge; Molina, Maria Graciela; Otiniano, L.; Asorey, Hernán Gonzalo; Sidelnik, Iván Pedro; Taboada, A.; Mayo García, R.; Rubi Montero, A. J.; Dasso, Sergio Ricardo
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The Latin American Giant Observatory (LAGO) is a ground-based observatory studying solar or high-energy astrophysics transient events. LAGO takes advantage of its distributed network of Water Cherenkov Detectors (WCDs) in Latin America as a tool to measure the secondary particle flux reaching the ground. These secondary particles are produced during the interaction between the modulated cosmic rays flux and the atmosphere. The LAGO WCDs are sensitive to secondary charged particles, high energy photons through pair creation and Compton scattering, and even neutrons thanks to, e.g., the deuteration of protons in the water volume. The pulse shape generated by these particles depends on several factors, such as the detector geometry, the water purity, the sensor response, or the reflectivity and diffusivity of the inner coating. Due to the decentralized nature of LAGO, these properties are different for each node. Additionally, the pulse shape depends on the convolution between the response of the central photomultiplier (PMT) to individual photons and the time distribution of the Cherenkov photons reaching the PMT. Typically, a WCD gives pulses with a sharp rise time (∼10ns) and a longer decay time (∼70ns). In this work, the WCD data used is acquired using the original LAGO data-acquisition system that digitizes pulses at a sampling rate of 40 MHz and 10 bits resolution on time windows of 400ns. Here, we apply unsupervised machine learning techniques to find patterns in the WCDs data and subsequently create groups, through clustering, that can be used to provide particle separation. We use data acquired from an individual WCD, showing that density-based clustering algorithms are suitable for automatic particle separation producing good candidate groups. Improved separation would help LAGO to reconstruct in situ the properties of primary cosmic rays flux. These results open the possibility to deploy machine learning-based models in our distributed detection network for onboard data analysis as an operative prototype, allowing detectors to be installed at very remote sites.
Fil: Torres Peralta, Ticiano Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología; Argentina
Fil: Molina, Maria Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología; Argentina. Istituto Nazionale di Geofisica e Vulcanologia; Italia
Fil: Otiniano, L.. Comisión Nacional de Investigación y Desarrollo Aeroespacial; Perú
Fil: Asorey, Hernán Gonzalo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Tecnología en Detección y Astropartículas. Comisión Nacional de Energía Atómica. Instituto de Tecnología en Detección y Astropartículas. Universidad Nacional de San Martín. Instituto de Tecnología en Detección y Astropartículas; Argentina
Fil: Sidelnik, Iván Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Gerencia de Ingeniería Nuclear (CAB). División Neutrones y Reactores; Argentina
Fil: Taboada, A.. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Tecnología en Detección y Astropartículas. Comisión Nacional de Energía Atómica. Instituto de Tecnología en Detección y Astropartículas. Universidad Nacional de San Martín. Instituto de Tecnología en Detección y Astropartículas; Argentina
Fil: Mayo García, R.. Centro de Investigaciones Energéticas Medioambientales y Tecnológicas;
Fil: Rubi Montero, A. J.. Centro de Investigaciones Energéticas Medioambientales y Tecnológicas;
Fil: Dasso, Sergio Ricardo. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina
Materia
CLUSTERING
MACHINE LEARNING
OPTICS
WATER CHERENKOV DETECTOR
Nivel de accesibilidad
acceso embargado
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/224332
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/224332
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Particle classification in the LAGO water Cherenkov detectors using clustering algorithmsTorres Peralta, Ticiano JorgeMolina, Maria GracielaOtiniano, L.Asorey, Hernán GonzaloSidelnik, Iván PedroTaboada, A.Mayo García, R.Rubi Montero, A. J.Dasso, Sergio RicardoCLUSTERINGMACHINE LEARNINGOPTICSWATER CHERENKOV DETECTORhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1The Latin American Giant Observatory (LAGO) is a ground-based observatory studying solar or high-energy astrophysics transient events. LAGO takes advantage of its distributed network of Water Cherenkov Detectors (WCDs) in Latin America as a tool to measure the secondary particle flux reaching the ground. These secondary particles are produced during the interaction between the modulated cosmic rays flux and the atmosphere. The LAGO WCDs are sensitive to secondary charged particles, high energy photons through pair creation and Compton scattering, and even neutrons thanks to, e.g., the deuteration of protons in the water volume. The pulse shape generated by these particles depends on several factors, such as the detector geometry, the water purity, the sensor response, or the reflectivity and diffusivity of the inner coating. Due to the decentralized nature of LAGO, these properties are different for each node. Additionally, the pulse shape depends on the convolution between the response of the central photomultiplier (PMT) to individual photons and the time distribution of the Cherenkov photons reaching the PMT. Typically, a WCD gives pulses with a sharp rise time (∼10ns) and a longer decay time (∼70ns). In this work, the WCD data used is acquired using the original LAGO data-acquisition system that digitizes pulses at a sampling rate of 40 MHz and 10 bits resolution on time windows of 400ns. Here, we apply unsupervised machine learning techniques to find patterns in the WCDs data and subsequently create groups, through clustering, that can be used to provide particle separation. We use data acquired from an individual WCD, showing that density-based clustering algorithms are suitable for automatic particle separation producing good candidate groups. Improved separation would help LAGO to reconstruct in situ the properties of primary cosmic rays flux. These results open the possibility to deploy machine learning-based models in our distributed detection network for onboard data analysis as an operative prototype, allowing detectors to be installed at very remote sites.Fil: Torres Peralta, Ticiano Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología; ArgentinaFil: Molina, Maria Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología; Argentina. Istituto Nazionale di Geofisica e Vulcanologia; ItaliaFil: Otiniano, L.. Comisión Nacional de Investigación y Desarrollo Aeroespacial; PerúFil: Asorey, Hernán Gonzalo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Tecnología en Detección y Astropartículas. Comisión Nacional de Energía Atómica. Instituto de Tecnología en Detección y Astropartículas. Universidad Nacional de San Martín. Instituto de Tecnología en Detección y Astropartículas; ArgentinaFil: Sidelnik, Iván Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Gerencia de Ingeniería Nuclear (CAB). División Neutrones y Reactores; ArgentinaFil: Taboada, A.. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Tecnología en Detección y Astropartículas. Comisión Nacional de Energía Atómica. Instituto de Tecnología en Detección y Astropartículas. Universidad Nacional de San Martín. Instituto de Tecnología en Detección y Astropartículas; ArgentinaFil: Mayo García, R.. Centro de Investigaciones Energéticas Medioambientales y Tecnológicas;Fil: Rubi Montero, A. J.. Centro de Investigaciones Energéticas Medioambientales y Tecnológicas;Fil: Dasso, Sergio Ricardo. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaElsevier Science2023-10info:eu-repo/date/embargoEnd/2024-04-19info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/224332Torres Peralta, Ticiano Jorge; Molina, Maria Graciela; Otiniano, L.; Asorey, Hernán Gonzalo; Sidelnik, Iván Pedro; et al.; Particle classification in the LAGO water Cherenkov detectors using clustering algorithms; Elsevier Science; Nuclear Instruments and Methods in Physics Research A: Accelerators, Spectrometers, Detectors and Associated Equipament; 1055; 10-2023; 1-50168-9002CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0168900223005478info:eu-repo/semantics/altIdentifier/doi/10.1016/j.nima.2023.168557info:eu-repo/semantics/embargoedAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:54:31Zoai:ri.conicet.gov.ar:11336/224332instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:54:32.066CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Particle classification in the LAGO water Cherenkov detectors using clustering algorithms
title Particle classification in the LAGO water Cherenkov detectors using clustering algorithms
spellingShingle Particle classification in the LAGO water Cherenkov detectors using clustering algorithms
Torres Peralta, Ticiano Jorge
CLUSTERING
MACHINE LEARNING
OPTICS
WATER CHERENKOV DETECTOR
title_short Particle classification in the LAGO water Cherenkov detectors using clustering algorithms
title_full Particle classification in the LAGO water Cherenkov detectors using clustering algorithms
title_fullStr Particle classification in the LAGO water Cherenkov detectors using clustering algorithms
title_full_unstemmed Particle classification in the LAGO water Cherenkov detectors using clustering algorithms
title_sort Particle classification in the LAGO water Cherenkov detectors using clustering algorithms
dc.creator.none.fl_str_mv Torres Peralta, Ticiano Jorge
Molina, Maria Graciela
Otiniano, L.
Asorey, Hernán Gonzalo
Sidelnik, Iván Pedro
Taboada, A.
Mayo García, R.
Rubi Montero, A. J.
Dasso, Sergio Ricardo
author Torres Peralta, Ticiano Jorge
author_facet Torres Peralta, Ticiano Jorge
Molina, Maria Graciela
Otiniano, L.
Asorey, Hernán Gonzalo
Sidelnik, Iván Pedro
Taboada, A.
Mayo García, R.
Rubi Montero, A. J.
Dasso, Sergio Ricardo
author_role author
author2 Molina, Maria Graciela
Otiniano, L.
Asorey, Hernán Gonzalo
Sidelnik, Iván Pedro
Taboada, A.
Mayo García, R.
Rubi Montero, A. J.
Dasso, Sergio Ricardo
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv CLUSTERING
MACHINE LEARNING
OPTICS
WATER CHERENKOV DETECTOR
topic CLUSTERING
MACHINE LEARNING
OPTICS
WATER CHERENKOV DETECTOR
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The Latin American Giant Observatory (LAGO) is a ground-based observatory studying solar or high-energy astrophysics transient events. LAGO takes advantage of its distributed network of Water Cherenkov Detectors (WCDs) in Latin America as a tool to measure the secondary particle flux reaching the ground. These secondary particles are produced during the interaction between the modulated cosmic rays flux and the atmosphere. The LAGO WCDs are sensitive to secondary charged particles, high energy photons through pair creation and Compton scattering, and even neutrons thanks to, e.g., the deuteration of protons in the water volume. The pulse shape generated by these particles depends on several factors, such as the detector geometry, the water purity, the sensor response, or the reflectivity and diffusivity of the inner coating. Due to the decentralized nature of LAGO, these properties are different for each node. Additionally, the pulse shape depends on the convolution between the response of the central photomultiplier (PMT) to individual photons and the time distribution of the Cherenkov photons reaching the PMT. Typically, a WCD gives pulses with a sharp rise time (∼10ns) and a longer decay time (∼70ns). In this work, the WCD data used is acquired using the original LAGO data-acquisition system that digitizes pulses at a sampling rate of 40 MHz and 10 bits resolution on time windows of 400ns. Here, we apply unsupervised machine learning techniques to find patterns in the WCDs data and subsequently create groups, through clustering, that can be used to provide particle separation. We use data acquired from an individual WCD, showing that density-based clustering algorithms are suitable for automatic particle separation producing good candidate groups. Improved separation would help LAGO to reconstruct in situ the properties of primary cosmic rays flux. These results open the possibility to deploy machine learning-based models in our distributed detection network for onboard data analysis as an operative prototype, allowing detectors to be installed at very remote sites.
Fil: Torres Peralta, Ticiano Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología; Argentina
Fil: Molina, Maria Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología; Argentina. Istituto Nazionale di Geofisica e Vulcanologia; Italia
Fil: Otiniano, L.. Comisión Nacional de Investigación y Desarrollo Aeroespacial; Perú
Fil: Asorey, Hernán Gonzalo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Tecnología en Detección y Astropartículas. Comisión Nacional de Energía Atómica. Instituto de Tecnología en Detección y Astropartículas. Universidad Nacional de San Martín. Instituto de Tecnología en Detección y Astropartículas; Argentina
Fil: Sidelnik, Iván Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Gerencia de Ingeniería Nuclear (CAB). División Neutrones y Reactores; Argentina
Fil: Taboada, A.. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Tecnología en Detección y Astropartículas. Comisión Nacional de Energía Atómica. Instituto de Tecnología en Detección y Astropartículas. Universidad Nacional de San Martín. Instituto de Tecnología en Detección y Astropartículas; Argentina
Fil: Mayo García, R.. Centro de Investigaciones Energéticas Medioambientales y Tecnológicas;
Fil: Rubi Montero, A. J.. Centro de Investigaciones Energéticas Medioambientales y Tecnológicas;
Fil: Dasso, Sergio Ricardo. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina
description The Latin American Giant Observatory (LAGO) is a ground-based observatory studying solar or high-energy astrophysics transient events. LAGO takes advantage of its distributed network of Water Cherenkov Detectors (WCDs) in Latin America as a tool to measure the secondary particle flux reaching the ground. These secondary particles are produced during the interaction between the modulated cosmic rays flux and the atmosphere. The LAGO WCDs are sensitive to secondary charged particles, high energy photons through pair creation and Compton scattering, and even neutrons thanks to, e.g., the deuteration of protons in the water volume. The pulse shape generated by these particles depends on several factors, such as the detector geometry, the water purity, the sensor response, or the reflectivity and diffusivity of the inner coating. Due to the decentralized nature of LAGO, these properties are different for each node. Additionally, the pulse shape depends on the convolution between the response of the central photomultiplier (PMT) to individual photons and the time distribution of the Cherenkov photons reaching the PMT. Typically, a WCD gives pulses with a sharp rise time (∼10ns) and a longer decay time (∼70ns). In this work, the WCD data used is acquired using the original LAGO data-acquisition system that digitizes pulses at a sampling rate of 40 MHz and 10 bits resolution on time windows of 400ns. Here, we apply unsupervised machine learning techniques to find patterns in the WCDs data and subsequently create groups, through clustering, that can be used to provide particle separation. We use data acquired from an individual WCD, showing that density-based clustering algorithms are suitable for automatic particle separation producing good candidate groups. Improved separation would help LAGO to reconstruct in situ the properties of primary cosmic rays flux. These results open the possibility to deploy machine learning-based models in our distributed detection network for onboard data analysis as an operative prototype, allowing detectors to be installed at very remote sites.
publishDate 2023
dc.date.none.fl_str_mv 2023-10
info:eu-repo/date/embargoEnd/2024-04-19
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/224332
Torres Peralta, Ticiano Jorge; Molina, Maria Graciela; Otiniano, L.; Asorey, Hernán Gonzalo; Sidelnik, Iván Pedro; et al.; Particle classification in the LAGO water Cherenkov detectors using clustering algorithms; Elsevier Science; Nuclear Instruments and Methods in Physics Research A: Accelerators, Spectrometers, Detectors and Associated Equipament; 1055; 10-2023; 1-5
0168-9002
CONICET Digital
CONICET
url http://hdl.handle.net/11336/224332
identifier_str_mv Torres Peralta, Ticiano Jorge; Molina, Maria Graciela; Otiniano, L.; Asorey, Hernán Gonzalo; Sidelnik, Iván Pedro; et al.; Particle classification in the LAGO water Cherenkov detectors using clustering algorithms; Elsevier Science; Nuclear Instruments and Methods in Physics Research A: Accelerators, Spectrometers, Detectors and Associated Equipament; 1055; 10-2023; 1-5
0168-9002
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0168900223005478
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.nima.2023.168557
dc.rights.none.fl_str_mv info:eu-repo/semantics/embargoedAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv embargoedAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
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instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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