Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory
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...
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| Autores principales: | , , , , , , , |
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| Formato: | Articulo |
| Lenguaje: | Inglés |
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2021
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/138175 |
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I19-R120-10915-138175 |
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dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
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SEDICI (UNLP) |
| language |
Inglés |
| topic |
Física Ciencias Exactas Data analysis Pattern recognition cluster finding calibration and fitting methods Large detector systems Particle identification methods particle physics astroparticle physics |
| spellingShingle |
Física Ciencias Exactas Data analysis Pattern recognition cluster finding calibration and fitting methods Large detector systems Particle identification methods particle physics astroparticle physics 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 Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory |
| topic_facet |
Física Ciencias Exactas Data analysis Pattern recognition cluster finding calibration and fitting methods Large detector systems Particle identification methods particle physics astroparticle physics |
| 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. |
| format |
Articulo Articulo |
| author |
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_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_sort |
Dova, María Teresa |
| title |
Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory |
| 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 |
| publishDate |
2021 |
| url |
http://sedici.unlp.edu.ar/handle/10915/138175 |
| work_keys_str_mv |
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Repositorios |
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1764820456831975428 |