Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory
- Autores
- Dova, María Teresa; Hansen, Patricia María; Mariazzi, Analisa Gabriela; Sciutto, Sergio Juan; Tueros, Matías Jorge; Vergara Quispe, Indira Dajhana; Wahlberg, Hernán Pablo; The Pierre Auger collaboration
- Año de publicación
- 2021
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- The atmospheric depth of the air shower maximum Xmax is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of Xmax are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of Xmax from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of Xmax. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long short-term memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed Xmax using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than 25 g/cm2 at energies above 2 × 1019 eV.
Instituto de Física La Plata - Materia
-
Física
Ciencias Exactas
Data analysis
Pattern recognition
cluster finding
calibration and fitting methods
Large detector systems
Particle identification methods
particle physics
astroparticle physics - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/138175
Ver los metadatos del registro completo
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Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger ObservatoryDova, María TeresaHansen, Patricia MaríaMariazzi, Analisa GabrielaSciutto, Sergio JuanTueros, Matías JorgeVergara Quispe, Indira DajhanaWahlberg, Hernán PabloThe Pierre Auger collaborationFísicaCiencias ExactasData analysisPattern recognitioncluster findingcalibration and fitting methodsLarge detector systemsParticle identification methodsparticle physicsastroparticle physicsThe atmospheric depth of the air shower maximum Xmax is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of Xmax are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of Xmax from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of Xmax. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long short-term memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed Xmax using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than 25 g/cm2 at energies above 2 × 1019 eV.Instituto de Física La Plata2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/138175enginfo:eu-repo/semantics/altIdentifier/issn/1748-0221info:eu-repo/semantics/altIdentifier/doi/10.1088/1748-0221/16/07/p07019info:eu-repo/semantics/altIdentifier/arxiv/2101.02946info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:32:12Zoai:sedici.unlp.edu.ar:10915/138175Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:32:13.205SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory |
title |
Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory |
spellingShingle |
Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory Dova, María Teresa Física Ciencias Exactas Data analysis Pattern recognition cluster finding calibration and fitting methods Large detector systems Particle identification methods particle physics astroparticle physics |
title_short |
Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory |
title_full |
Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory |
title_fullStr |
Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory |
title_full_unstemmed |
Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory |
title_sort |
Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory |
dc.creator.none.fl_str_mv |
Dova, María Teresa Hansen, Patricia María Mariazzi, Analisa Gabriela Sciutto, Sergio Juan Tueros, Matías Jorge Vergara Quispe, Indira Dajhana Wahlberg, Hernán Pablo The Pierre Auger collaboration |
author |
Dova, María Teresa |
author_facet |
Dova, María Teresa Hansen, Patricia María Mariazzi, Analisa Gabriela Sciutto, Sergio Juan Tueros, Matías Jorge Vergara Quispe, Indira Dajhana Wahlberg, Hernán Pablo The Pierre Auger collaboration |
author_role |
author |
author2 |
Hansen, Patricia María Mariazzi, Analisa Gabriela Sciutto, Sergio Juan Tueros, Matías Jorge Vergara Quispe, Indira Dajhana Wahlberg, Hernán Pablo The Pierre Auger collaboration |
author2_role |
author author author author author author author |
dc.subject.none.fl_str_mv |
Física Ciencias Exactas Data analysis Pattern recognition cluster finding calibration and fitting methods Large detector systems Particle identification methods particle physics astroparticle physics |
topic |
Física Ciencias Exactas Data analysis Pattern recognition cluster finding calibration and fitting methods Large detector systems Particle identification methods particle physics astroparticle physics |
dc.description.none.fl_txt_mv |
The atmospheric depth of the air shower maximum Xmax is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of Xmax are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of Xmax from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of Xmax. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long short-term memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed Xmax using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than 25 g/cm2 at energies above 2 × 1019 eV. Instituto de Física La Plata |
description |
The atmospheric depth of the air shower maximum Xmax is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of Xmax are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of Xmax from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of Xmax. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long short-term memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed Xmax using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than 25 g/cm2 at energies above 2 × 1019 eV. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Articulo http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
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article |
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publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/138175 |
url |
http://sedici.unlp.edu.ar/handle/10915/138175 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/issn/1748-0221 info:eu-repo/semantics/altIdentifier/doi/10.1088/1748-0221/16/07/p07019 info:eu-repo/semantics/altIdentifier/arxiv/2101.02946 |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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