Feasibility projections indicate that an optimized NINJA-like detector at the SS floor location can deliver competitive constraints on active-sterile mixing parameters for eV-scale mass-squared differences.
Electron/pion separation with an Emulsion Cloud Chamber by using a Neural Network
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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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hep-ph 1years
2026 1verdicts
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Feasibility study of light sterile neutrino searches with a future NINJA-like detector
Feasibility projections indicate that an optimized NINJA-like detector at the SS floor location can deliver competitive constraints on active-sterile mixing parameters for eV-scale mass-squared differences.