Computer Science – Artificial Intelligence
Scientific paper
Aug 1995
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1995lnf..rept...95a&link_type=abstract
Technical Report, LNF-P-95-046; DE97-732490 Lab. Nazionale di Frascati
Computer Science
Artificial Intelligence
Expert Systems, Neural Nets, Cosmic Rays, Electrons, Positrons, Radiation Effects, Artificial Intelligence, Radiation Detectors, Calorimeters, Imaging Techniques, Silicon, Tungsten
Scientific paper
A data analysis based on artificial neural network classifiers has been done to identify cosmic ray electrons and positrons detected with the balloon-borne NMSU/Wizard-TS93 experiment. The information is provided by two ancillary and independent particle detectors: a transition radiation detector and a silicon-tungsten imaging calorimeter. Electrons and positrons measured during the flight have been identified with background rejection factors of 80 +/- 3 and 500 +/- 37 at signal efficiencies of 72 +/- 3% and 86 +/- 2% for the transition radiation detector and the silicon-tungsten imaging calorimeter, respectively. The ability of the artificial neural network classifiers to perform a careful multidimensional analysis surpasses the results achieved by conventional methods.
Aversa F.
Barbiellini Guido
Basini Giuseppe
Bellotti Roberto
Bidoli V.
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