Data analysis in relativistic nuclear physics using neural network

Abstract

Two neural network algorithms for data analysis in relativistic nuclear physics are presented. A neural network technique (Hopfield method) is used in order to reconstruct particle tracks starting from a data set obtained with a coordinate detector system. An algorithm for circles recognition using deformable templates is carried out and its performances are studied. The technical limitations of the detectors, which in real situation prevent the possibility to reconstruct hits right on the circle, and presence of the noise points are taken into account.