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

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.

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Cited by in corpus (6)

• Track reconstruction in the emulsion-lead target of the OPERA experiment using the ESS microscope
• Study of the effects induced by lead on the emulsion films of the OPERA experiment
• Large-angle scattering of multi-GeV muons on thin Lead targets
• Unravelling the richness of dark sector by FASER$\nu$
• Nuclear emulsions in the OPERA experiment
• Segmentation of EM showers for neutrino experiments with deep graph neural networks