The mass composition plays an important role for understanding the origin of the UHE cosmic rays. The composition in the energy region at and beyond the knee is important because it is related to the sites of cosmic ray productions and accelerations. In order to perform the composition measurement, an artificial neural network (ANN) has been implemented, it is based on a set of composition estimators obtained by a detailed study of the lateral particle density distribution. Showers induced by protons, He nuclei, CNO group and iron nuclei have been generated in the energy region (30-10000) TeV, the lateral particle density distribution was estimated. In this paper the estimators are presented, the performance of the mass discrimination is discussed.

Cirillo, A., Mari, S.M., Montini, P. (2011). Cosmic ray elemental composition study by using an artificial neural network based on the measurement of the lateral particle density distribution in showers induced by primaries in the 30-10000 TeV energy region. In 32ND INTERNATIONAL COSMIC RAY CONFERENCE (pp.1-4). Beijing : IHEP [10.7529/ICRC2011/V01/0691].

Cosmic ray elemental composition study by using an artificial neural network based on the measurement of the lateral particle density distribution in showers induced by primaries in the 30-10000 TeV energy region

MARI, Stefano Maria;Montini P.
2011

Abstract

The mass composition plays an important role for understanding the origin of the UHE cosmic rays. The composition in the energy region at and beyond the knee is important because it is related to the sites of cosmic ray productions and accelerations. In order to perform the composition measurement, an artificial neural network (ANN) has been implemented, it is based on a set of composition estimators obtained by a detailed study of the lateral particle density distribution. Showers induced by protons, He nuclei, CNO group and iron nuclei have been generated in the energy region (30-10000) TeV, the lateral particle density distribution was estimated. In this paper the estimators are presented, the performance of the mass discrimination is discussed.
Cirillo, A., Mari, S.M., Montini, P. (2011). Cosmic ray elemental composition study by using an artificial neural network based on the measurement of the lateral particle density distribution in showers induced by primaries in the 30-10000 TeV energy region. In 32ND INTERNATIONAL COSMIC RAY CONFERENCE (pp.1-4). Beijing : IHEP [10.7529/ICRC2011/V01/0691].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/175209
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