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

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.