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27 April 2024

» arxiv » physics/0701192

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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 ; 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 ;
Date:	17 Jan 2007
Subject:	Data Analysis, Statistics and Probability
Abstract:	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.
Source:	arXiv, physics/0701192
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