Signal Classification for Acoustic Neutrino Detection

TL;DR

This paper explores machine learning-based signal classification methods to distinguish neutrino signals from diverse transient background noise in deep-sea acoustic detection, achieving high accuracy with ensemble classifiers.

Contribution

It introduces a machine learning classification system tailored for deep-sea acoustic neutrino detection, demonstrating high accuracy with Random Forest and Boosting Trees.

Findings

Testing error around 1% for strong classifiers

Effective use of sensor clusters improves accuracy

Machine learning enhances signal-background discrimination

Abstract

This article focuses on signal classification for deep-sea acoustic neutrino detection. In the deep sea, the background of transient signals is very diverse. Approaches like matched filtering are not sufficient to distinguish between neutrino-like signals and other transient signals with similar signature, which are forming the acoustic background for neutrino detection in the deep-sea environment. A classification system based on machine learning algorithms is analysed with the goal to find a robust and effective way to perform this task. For a well-trained model, a testing error on the level of one percent is achieved for strong classifiers like Random Forest and Boosting Trees using the extracted features of the signal as input and utilising dense clusters of sensors instead of single sensors.