Composition Classification of Ultra-High Energy Cosmic Rays (2020)
- Authors:
- Autor USP: PEIXOTO, CARLOS JOSÉ TODERO - EEL
- Unidade: EEL
- DOI: 10.3390/e22090998
- Assunto: RAIOS CÓSMICOS
- Keywords: cosmic rays; ultra high energy; mass composition; feature selection; deep learning
- Agências de fomento:
- Language: Inglês
- Abstract: The study of cosmic rays remains as one of the most challenging research fields in Physics. From the many questions still open in this area, knowledge of the type of primary for each event remains as one of the most important issues. All of the cosmic rays observatories have been trying to solve this question for at least six decades, but have not yet succeeded. The main obstacle is the impossibility of directly detecting high energy primary events, being necessary to use Monte Carlo models and simulations to characterize generated particles cascades. This work presents the results attained using a simulated dataset that was provided by the Monte Carlo code CORSIKA, which is a simulator of high energy particles interactions with the atmosphere, resulting in a cascade of secondary particles extending for a few kilometers (in diameter) at ground level. Using this simulated data, a set of machine learning classifiers have been designed and trained, and their computational cost and effectiveness compared, when classifying the type of primary under ideal measuring conditions. Additionally, a feature selection algorithm has allowed for identifying the relevance of the considered features. The results confirm the importance of the electromagnetic-muonic component separation from signal data measured for the problem. The obtained results are quite encouraging and open new work lines for future more restrictive simulations.
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- Source:
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- Este artigo é de acesso aberto
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- Cor do Acesso Aberto: gold
- Licença: cc-by
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ABNT
HERRERA, Luis Javier et al. Composition Classification of Ultra-High Energy Cosmic Rays. Entropy, v. 22, n. 9, p. 1-12, 2020Tradução . . Disponível em: https://doi.org/10.3390/e22090998. Acesso em: 26 dez. 2025. -
APA
Herrera, L. J., Peixoto, C. J. T., Baños, O., Carceller, J. M., Carrillo, F., & Guillen, A. (2020). Composition Classification of Ultra-High Energy Cosmic Rays. Entropy, 22( 9), 1-12. doi:10.3390/e22090998 -
NLM
Herrera LJ, Peixoto CJT, Baños O, Carceller JM, Carrillo F, Guillen A. Composition Classification of Ultra-High Energy Cosmic Rays [Internet]. Entropy. 2020 ;22( 9): 1-12.[citado 2025 dez. 26 ] Available from: https://doi.org/10.3390/e22090998 -
Vancouver
Herrera LJ, Peixoto CJT, Baños O, Carceller JM, Carrillo F, Guillen A. Composition Classification of Ultra-High Energy Cosmic Rays [Internet]. Entropy. 2020 ;22( 9): 1-12.[citado 2025 dez. 26 ] Available from: https://doi.org/10.3390/e22090998 - 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
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- Signatures of ultra-high energy cosmic ray sources in large-scale anisotropy measurements
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- 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
Informações sobre o DOI: 10.3390/e22090998 (Fonte: oaDOI API)
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