Performance of the MIND detector at a Neutrino Factory using realistic muon reconstruction

TL;DR

This paper evaluates the performance of the MIND detector at a Neutrino Factory with realistic muon pattern recognition, demonstrating maintained sensitivity to key neutrino oscillation parameters through optimized analysis methods.

Contribution

It introduces a realistic muon pattern recognition approach for MIND and re-optimizes the analysis to preserve sensitivity to neutrino oscillation parameters.

Findings

Realistic pattern recognition improves detector efficiency.

Optimized analysis maintains sensitivity to theta_13 and delta_CP.

Background suppression is achieved without sacrificing signal efficiency.

Abstract

A Neutrino Factory producing an intense beam composed of nu_e(nubar_e) and nubar_mu(nu_mu) from muon decays has been shown to have the greatest sensitivity to the two currently unmeasured neutrino mixing parameters, theta_13 and delta_CP . Using the `wrong-sign muon' signal to measure nu_e to nu_mu(nubar_e to nubar_mu) oscillations in a 50 ktonne Magnetised Iron Neutrino Detector (MIND) sensitivity to delta_CP could be maintained down to small values of theta_13. However, the detector efficiencies used in previous studies were calculated assuming perfect pattern recognition. In this paper, MIND is re-assessed taking into account, for the first time, a realistic pattern recognition for the muon candidate. Reoptimisation of the analysis utilises a combination of methods, including a multivariate analysis similar to the one used in MINOS, to maintain high efficiency while suppressing backgrounds, ensuring that the signal selection efficiency and the background levels are comparable or better than the ones in previous analyses.