Improving the hierarchy sensitivity of ICAL using neural network

TL;DR

This paper enhances the sensitivity of the ICAL neutrino detector to the neutrino mass hierarchy by employing adaptive neural networks to better identify relevant neutrino events, achieving a 3 sigma significance.

Contribution

The study introduces the use of adaptive neural networks to improve event selection in ICAL, increasing hierarchy sensitivity beyond previous methods.

Findings

Hierarchy sensitivity reaches 3 sigma significance.

Neural network-based event identification improves detection efficiency.

Enhanced hierarchy signature detection in neutrino events.

Abstract

Atmospheric neutrino experiments can determine the neutrino mass hierarchy for any value of $\delta_{CP}$. The Iron Calorimeter (ICAL) detector at the India-based Neutrino Observatory can distinguish between the charged current interactions of $\nu_\mu$ and $\bar{\nu}_\mu$ by determining the charge of the produced muon. Hence it is particularly well suited to determine the hierarchy. The hierarchy signature is more prominent in neutrinos with energy of a few GeV and with pathlength of a few thousand kilometers, $\textit{i.e.}$ neutrinos whose direction is not close to horizontal. We use adaptive neural networks to identify such events with good efficiency and good purity. The hierarchy sensitivity, calculated from these selected events, reaches a $3 \sigma$ level, with a $\Delta \chi^2$ of 9.