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

L. Arrabito , D. Autiero , C. Bozza , S. Buontempo , Y. Caffari , L. Consiglio , M. Cozzi , N. D'Ambrosio

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G. De Lellis M. De Serio F. Di Capua D. Di Ferdinando N. Di Marco A. Ereditato L. S. Esposito S. Gagnebin G. Giacomelli M. Giorgini G. Grella M. Hauger M. Ieva J. Janicsko Csathy F. Juget I. Kreslo I. Laktineh A. Longhin G. Mandrioli A. Marotta J. Marteau P. Migliozzi P. Monacelli U. Moser M. T. Muciaccia A. Pastore L. Patrizii C. Pistillo M. Pozzato G. Romano G. Rosa A. Russo N. Savvinov A. Schembri L. Scotto Lavina S. Simone M. Sioli C. Sirignano G. Sirri P. Strolin V. Tioukov

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classification ⚛️ physics.data-an

keywords electronpionalgorithmseparationemulsionbeamsbeenchamber

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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 $e/\pi$ 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.

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

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