Composition Classification of Ultra-High Energy Cosmic Rays
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AuthorHerrera Maldonado, Luis Javier; Baños Legrán, Oresti; Carceller López, Juan Miguel; Carrillo Pérez, Francisco; Guillén Perales, Alberto
Cosmic raysUltra-high energyMass compositionFeature selectionDeep learning
Herrera, L.J.; Todero Peixoto, C.J.; Baños, O.; Carceller, J.M.; Carrillo, F.; Guillén, A. Composition Classification of Ultra-High Energy Cosmic Rays. Entropy 2020, 22, 998. [DOI: https://doi.org/10.3390/e22090998]
SponsorshipSpanish Ministry of Science, Innovation and Universities FPA2017-85197-P RTI2018-101674-B-I00; European Union (EU); CENAPAD-SP (Centro Nacional de Processamento de Alto Desempenho em Sao Paulo) UNICAMP/FINEP - MCT; Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP); National Council for Scientific and Technological Development (CNPq) 2016/19764-9; 404993/2016-8
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.