Neural Networks as a Statistic Diagnostic Tool for Mass Composition at the Highest Energies

Details Neural Networks as a Statistic Diagnostic Tool for Mass Composition at the Highest Energies Neural Networks as a Statistic Diagnostic Tool for Mass Composition at the Highest Energies

Jul 2003

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2003icrc....2..587t&link_type=abstract

Proceedings of the 28th International Cosmic Ray Conference. July 31-August 7, 2003. Trukuba, Japan. Under the auspices of the I

Physics

Scientific paper

The performance of a neural network based multivariate analysis for the determination of mass composition at the highest energies is studied. We use a simulation chain that includes the code AIRES plus a surface array simulator configured to emulate the Auger Southern Observatory. A very large set of more than 30,000 showers simulated with great detail is used in our analysis. A set of multiple observables is used as input for the neural network algorithm that is tuned for optimum determination of the primary composition. The most relevant characteristics of the method are analyzed, including its capability for hadronic model indep endent composition assignment.

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