Search for a 3+1 Sterile Neutrino with the MicroBooNE Experiment using Deep-Learning-Based Reconstruction

UNSPECIFIED (2022) Search for a 3+1 Sterile Neutrino with the MicroBooNE Experiment using Deep-Learning-Based Reconstruction. Other. UNSPECIFIED.

Abstract

In this note we present the methods and sensitivity for a search for sterile neutrinos based on the 3+1 model in the MicroBooNE experiment. The recently released results by MicroBooNE show no sign of the MiniBooNE/LSND low-energy-excess anomaly. The 3+1 model examined here expands on the standard model of neutrinos by adding a fourth neutrino flavor and is not necessarily ruled out by the lack of a low energy excess. The search presented relies on Deep-Learning-based reconstruction tools and looks for charged current quasi-elastic-like (CCQE-like) events kinematically consistent with a 2-body interaction. Using two orthogonal samples of CCQE electron neutrino events and CCQE muon neutrino events, we test this model allowing for electron neutrino appearance, electron neutrino disappearance, and muon neutrino disappearance. We present the sensitivity to the oscillation parameters using the Wilks’ theorem exclusion confidence levels. We demonstrate the use of a minimizer to find the best fit and discuss the effect of using a Feldman-Cousins’ procedure instead of Wilks’ theorem to determine sensitivity.