Electron/pion separation with an Emulsion Cloud Chamber by using a Neural Network

Arrabito, L.; Autiero, D.; Bozza, C.; Buontempo, S.; Caffari, Y.; Consiglio, L.; Cozzi, M.; D'Ambrosio, N.; De Lellis, G.; De Serio, M.; Di Capua, F.; Di Ferdinando, D.; Di Marco, N.; Ereditato, A.; Esposito, L. S.; Gagnebin, S.; Giacomelli, G.; Giorgini, M.; Grella, G.; Hauger, M.; Ieva, M.; Csathy, J. Janicsko; Juget, F.; Kreslo, I.; Laktineh, I.; Longhin, A.; Mandrioli, G.; Marotta, A.; Marteau, J.; Migliozzi, P.; Monacelli, P.; Moser, U.; Muciaccia, M. T.; Pastore, A.; Patrizii, L.; Pistillo, C.; Pozzato, M.; Romano, G.; Rosa, G.; Russo, A.; Savvinov, N.; Schembri, A.; Lavina, L. Scotto; Simone, S.; Sioli, M.; Sirignano, C.; Sirri, G.; Strolin, P.; Tioukov, V.

Electron/pion separation with an Emulsion Cloud Chamber by using a Neural Network

Authors

Description

We have studied the performance of a new algorithm for electron/pion separation in an Emulsion Cloud Chamber (ECC) made of lead and nuclear emulsion films. The software for separation consists of two parts: a shower reconstruction algorithm and a Neural Network that assigns to each reconstructed shower the probability to be an electron or a pion. The performance has been studied for the ECC of the OPERA experiment [1]. The $e/π$ separation algorithm has been optimized by using a detailed Monte Carlo simulation of the ECC and tested on real data taken at CERN (pion beams) and at DESY (electron beams). The algorithm allows to achieve a 90% electron identification efficiency with a pion misidentification smaller than 1% for energies higher than 2 GeV.