Application of Deep Neural Networks to Event Type Classification in IceCube

TL;DR

This paper introduces a deep neural network classifier based on InceptionResNet architecture for event type classification in IceCube, improving the accuracy and broadening the application scope of neutrino interaction analysis.

Contribution

It presents a novel multi-task deep neural network model tailored for classifying neutrino event types in IceCube, leveraging modern image recognition architectures.

Findings

High classification accuracy demonstrated.

Multi-task learning enhances performance.

Potential applications in neutrino physics analysis.

Abstract

The IceCube Neutrino Observatory is able to measure the all-flavor neutrino flux in the energy range between 100 GeV and several PeV. Due to the different features of the neutrino interactions and the geometry of the detector, all high-level analyses require a selection of suitable events as a first step. However, presently, no algorithm exists that gives a generic prediction of an event's underlying interaction type. One possible solution to this is the use of deep neural networks similar to the ones commonly used for 2D image recognition. The classifier that we present here is based on the modern InceptionResNet architecture and includes multi-task learning in order to broaden the field of application and increase the overall accuracy of the result. We provide a detailed discussion of the network's architecture, examine the performance of the classifier for event type classification and explain possible applications in IceCube.