Particle identification by Cherenkov ring imaging using a neutral network approach

The performance of a back-propagation neural network for particle identification using a RICH detector has been studied. When trained on 8000 simulated events in 14 iterations using a general back-propagation algorithm it correctly identifies 86% of the events out of a sample of 1000 experimentally measured pion and proton events at 3.5 GeV/c beam momentum. The identification efficiency is 70%. This is compatible with what is obtained by conventional, but mathematically much more complicated, identification algorithms. (orig.)