@misc{10481/64536,
year = {2020},
url = {http://hdl.handle.net/10481/64536},
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.},
organization = {Spanish Ministry of Science, Innovation and Universities 	
FPA2017-85197-P
RTI2018-101674-B-I00},
organization = {European Union (EU)},
organization = {CENAPAD-SP (Centro Nacional de Processamento de Alto Desempenho em Sao Paulo) 	
UNICAMP/FINEP - MCT},
organization = {Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)},
organization = {National Council for Scientific and Technological Development (CNPq)

2016/19764-9},
organization = {404993/2016-8},
publisher = {MDPI},
keywords = {Cosmic rays},
keywords = {Ultra-high energy},
keywords = {Mass composition},
keywords = {Feature selection},
keywords = {Deep learning},
title = {Composition Classification of Ultra-High Energy Cosmic Rays},
doi = {10.3390/e22090998},
author = {Herrera Maldonado, Luis Javier and Baños Legrán, Oresti and Carceller López, Juan Miguel and Carrillo Pérez, Francisco and Guillén Perales, Alberto},
}