Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory
dc.audience | publishedVersion | |
dc.citation.title | Journal of Instrumentation | es |
dc.citation.volume | 16 | |
dc.creator | Freir, M. M. | |
dc.creator | Micheletti, M. I. | |
dc.creator | The Pierre Auger collaboration | |
dc.date.accessioned | 2022-04-07T16:00:01Z | |
dc.date.available | 2022-04-07T16:00:01Z | |
dc.date.issued | 2021-07-14 | |
dc.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 shortterm 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. | es |
dc.description.fil | Fil: Freir, M. M. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Física de Rosario (IFIR-CONICET); Argentina. | es |
dc.description.fil | Fil: Micheletti, M. I. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Física de Rosario (IFIR-CONICET); Argentina. | es |
dc.format | application/pdf | |
dc.format.extent | 1-27 | es |
dc.identifier.issn | 1748-0221 | es |
dc.identifier.uri | http://hdl.handle.net/2133/23338 | |
dc.language.iso | eng | es |
dc.publisher | IOP Publishing | es |
dc.rights | openAccess | es |
dc.rights.holder | The Pierre Auger collaboration | es |
dc.rights.holder | Freir, M. M. | es |
dc.rights.holder | Micheletti, M. I. | es |
dc.rights.text | Attribution 4.0 International (CC BY 4.0) | es |
dc.rights.uri | http://creativecommons.org/licenses/by/2.5/ar/ | * |
dc.subject | Data analysis | es |
dc.subject | Pattern recognition | es |
dc.subject | Cluster finding | es |
dc.subject | Calibration and fitting methods | es |
dc.subject | Large detector systems for particle and astroparticle physics | es |
dc.subject | Particle identification methods | es |
dc.title | Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory | es |
dc.type | publishedVersion | |
dc.type | article | |
dc.type | artículo | |
dc.type.collection | articulo | |
dc.type.version | publishedVersion |