Cosmic-ray discrimination capabilities of /ΔE-/E silicon nuclear telescopes using neural networks

Details Cosmic-ray discrimination capabilities of /ΔE-/E silicon nuclear telescopes using neural networks Cosmic-ray discrimination capabilities of /ΔE-/E silicon nuclear telescopes using neural networks

Feb 2000

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2000nimpa.440..438a&link_type=abstract

Nuclear Instruments and Methods in Physics Research Section A, Volume 440, Issue 2, p. 438-445.

Physics

Scientific paper

An isotope classifier of cosmic-ray events collected by space detectors has been implemented using a multi-layer perceptron neural architecture. In order to handle a great number of different isotopes a modular architecture of the ``mixture of experts'' type is proposed. The performance of this classifier has been tested on simulated data and has been compared with a ``classical'' classifying procedure. The quantitative comparison with traditional techniques shows that the neural approach has classification performances comparable - within /1% - with that of the classical one, with efficiency of the order of /98%. A possible hardware implementation of such a kind of neural architecture in future space missions is considered.

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