Distinguishing primary cosmic-ray composition with artificial neural networks.

Details Distinguishing primary cosmic-ray composition with artificial neural networks. Distinguishing primary cosmic-ray composition with artificial neural networks.

Mar 1997

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1997hepnp..21..205l&link_type=abstract

High Energ. Phys. Nucl. Phys., Vol. 21, No. 3, p. 205 - 210

Physics

Cosmic Rays: High Energy

Scientific paper

The authors used artificial neural networks to distinguish superhigh energy cosmic-ray protons (p) and nuclei (N) with Monte Carlo family data in a mountain emulsion chamber experiment. The result shows that when the visible energy of a family is larger than 500 TeV, about 80% of p and N can be correctly selected and more than 70% can be selected in the region between 100 and 500 TeV.

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