Identification of cosmic ray electrons and positrons by neural network
Abstract
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.
- Publication:
-
Technical Report
- Pub Date:
- August 1995
- Bibcode:
- 1995lnf..rept...95A
- Keywords:
-
- Expert Systems;
- Neural Nets;
- Cosmic Rays;
- Electrons;
- Positrons;
- Radiation Effects;
- Artificial Intelligence;
- Radiation Detectors;
- Calorimeters;
- Imaging Techniques;
- Silicon;
- Tungsten;
- Space Radiation