A Search for Neutrino Sources with Cascade Events in IceCube

TL;DR

This paper introduces a new neutrino detection dataset using deep neural networks to select cascade events, enhancing sensitivity to southern sky sources and transient neutrino phenomena in IceCube.

Contribution

The work presents a novel DNN-based cascade event selection method that improves detection sensitivity and enables near real-time analysis for astrophysical neutrino sources.

Findings

2-3 times improved sensitivity to southern sky sources

Lower energy threshold and background rate in the dataset

Enhanced capability to detect transient and galactic plane neutrino sources

Abstract

IceCube has discovered a flux of astrophysical neutrinos, and more recently has used muon-neutrino datasets to present evidence for one source; a flaring blazar known as TXS 0506+056. However, the sources responsible for the majority of the astrophysical neutrino flux remain elusive. Opening up new channels for detection can improve sensitivity and increase the discovery potential. In this work we present a new neutrino dataset relying heavily on Deep-Neural-Networks (DNN) to select cascade events produced from neutral-current interactions of all flavors and charged-current interactions with flavors other than muon-neutrino. The speed of DNN processing makes it possible to select events in near realtime with a single GPU. Cascade events have reduced angular resolution when compared to muon-neutrino events, however the resulting dataset has a lower energy threshold in the southern sky and a lower background rate. These benefits lead to an factor of 2-3 improvement in sensitivity to sources in the Southern Sky when compared to muon-neutrino datasets. This dataset is particularly promising for identifying transient neutrino sources in the Southern Sky and neutrino production from the galactic plane.