A neural network for positron identification by transition radiation detector

Abstract A neural network algorithm has been applied in order to distinguish positrons from protons by a transition radiation detector (TRD). New variables are introduced, that simultaneously take into account spatial and energy TRD information. This method is found to be better than the one based on classical analysis: the results improve the detector performance in particle identification for efficiency higher than 90%. The high accuracy achieved with this method is used to identify positrons versus protons with 3 × 10 −3 contamination, as required by TRAMP-SI cosmic ray space experiment on the NASA Balloon-Borne Magnet Facility.