Deep learning techniques applied to the physics of extensive air showers (2019)
- Authors:
- Autor USP: PEIXOTO, CARLOS JOSÉ TODERO - EEL
- Unidade: EEL
- DOI: 10.1016/j.astropartphys.2019.03.001
- Subjects: REDES NEURAIS; RAIOS CÓSMICOS
- Keywords: Machine learning; Deep neural networks; Ultra-high-energy; Cosmic rays; Pierre auger observatory
- Agências de fomento:
- Language: Inglês
- Abstract: Deep neural networks are a powerful technique that have found ample applications in several branches of physics. In this work, we apply deep neural networks to a specific problem of cosmic ray physics: the estimation of the muon content of extensive air showers when measured at the ground. As a working case, we explore the performance of a deep neural network applied to large sets of simulated signals recorded for the water-Cherenkov detectors of the Surface Detector of the Pierre Auger Observatory. The inner structure of the neural network is optimized through the use of genetic algorithms. To obtain a prediction of the recorded muon signal in each individual detector, we train neural networks with a mixed sample of simulated events that contain light, intermediate and heavy nuclei. When true and predicted signals are compared at detector level, the primary values of the Pearson correlation coefficients are above 95%. The relative errors of the predicted muon signals are below 10% and do not depend on the event energy, zenith angle, total signal size, distance range or the hadronic model used to generate the events.
- Imprenta:
- Source:
- Título do periódico: Astroparticle physics
- ISSN: 09276505
- Volume/Número/Paginação/Ano: v. 111, p.12-22, 2019
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: green
-
ABNT
GUILLÉN, A. et al. Deep learning techniques applied to the physics of extensive air showers. Astroparticle physics, v. 111, p. 12-22, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.astropartphys.2019.03.001. Acesso em: 05 maio 2024. -
APA
GUILLÉN, A., Bueno, A., Carceller, J. M., Velazquez, J. C. M., Rubio, G., Peixoto, C. J. T., & Sanchez-Lucas, P. (2019). Deep learning techniques applied to the physics of extensive air showers. Astroparticle physics, 111, 12-22. doi:10.1016/j.astropartphys.2019.03.001 -
NLM
GUILLÉN A, Bueno A, Carceller JM, Velazquez JCM, Rubio G, Peixoto CJT, Sanchez-Lucas P. Deep learning techniques applied to the physics of extensive air showers [Internet]. Astroparticle physics. 2019 ; 111 12-22.[citado 2024 maio 05 ] Available from: https://doi.org/10.1016/j.astropartphys.2019.03.001 -
Vancouver
GUILLÉN A, Bueno A, Carceller JM, Velazquez JCM, Rubio G, Peixoto CJT, Sanchez-Lucas P. Deep learning techniques applied to the physics of extensive air showers [Internet]. Astroparticle physics. 2019 ; 111 12-22.[citado 2024 maio 05 ] Available from: https://doi.org/10.1016/j.astropartphys.2019.03.001 - Estimating the Depth of Shower Maximum using the Surface Detectors of the Pierre Auger Observatory
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- Monte Carlo performance studies for the site selection of the Cherenkov Telescope Array
- Probing the radio emission from air showers with polarization measurements
- Searches for large-scale anisotropy in the arrival directi ons of cosmic rays detected above energy of '10 POT. 19' eV at the Pierre Auger Observatory and the telescope array
- Reconstruction of inclined air showers detected with the Pierre Auger Observatory
- Gerador trifásico de baixo custo para o ensino de física
- Ultrahigh Energy Neutrinos at the Pierre Auger Observatory
- A Targeted Search for Point Sources of EeV Neutrons
- Pierre Auger Observatory and Telescope Array: Joint Contributions to the 33rd International Cosmic Ray Conference (ICRC 2013)
Informações sobre o DOI: 10.1016/j.astropartphys.2019.03.001 (Fonte: oaDOI API)
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