A shower identification method using a Bayesian statistical model

Due to the scale expansion and complexity of experiments in high energy physics experiment, storing data on a database and techniques of knowledge discovery are considered to be useful for efficient storage and analysis of data. We present a new method based on Bayesian statistics to identify electrons and charged pions in shower counters. We designed an ideal shower counter and studied the efficiency using Monte Carlo simulation based on Geant4. Without having any bias, e.g. tracker information, purity of more than 97% have been achieved for identification of both particles.