Incorporating Inelasticity Reconstruction into Neutrino Mass Ordering Studies with IceCube

TL;DR

This paper develops neural network-based inelasticity reconstruction methods to improve neutrino mass ordering determination using IceCube data, leveraging differences in neutrino and antineutrino interactions.

Contribution

It introduces new inelasticity reconstruction algorithms and demonstrates their potential to enhance NMO sensitivity in IceCube analyses.

Findings

Neural network reconstructions achieve accurate inelasticity estimates.

Adding inelasticity as an observable improves NMO sensitivity.

The methods are applicable to IceCube DeepCore and IceCube Upgrade data.

Abstract

Earth's matter affects the oscillation of atmospheric neutrinos and antineutrinos differently depending on the neutrino mass ordering (NMO). As more neutrinos than antineutrinos are expected to be detected in the IceCube detector, this matter effect can be used to probe the NMO. The fraction of energy transferred to the nucleon during a neutrino interaction, known as the inelasticity, has a different distribution for neutrinos and antineutrinos because of their opposite chirality. This can in theory be used to statistically separate neutrinos from antineutrinos, but hasn't been exploited in IceCube DeepCore analyses yet. To this end, two new inelasticity reconstructions were developed using a graph neural network and an ensemble of two-dimensional convolutional neural networks. This presentation discusses the development and performances of these reconstruction algorithms. The inelasticity is then used as a fourth observable, along with the particle energy, direction and flavor, to calculate new NMO sensitivities and determine the impact of adding the inelasticity in the measurement of the NMO with the IceCube DeepCore and upcoming IceCube Upgrade detectors.