Signal Classification for Acoustic Neutrino Detection

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.

8 Pages, 6 Figures, ARENA 2010 Conference Proceedings

References in corpus (2)

• AMADEUS - The Acoustic Neutrino Detection Test System of the ANTARES Deep-Sea Neutrino Telescope
• A Deep Sea Telescope for High Energy Neutrinos

Cited by in corpus (5)

• Contributions to the 32nd International Cosmic Ray Conference (ICRC 2011) by the ANTARES collaboration
• Status and Recent Results of the Acoustic Neutrino Detection Test System AMADEUS
• Simulation Chain for Acoustic Ultra-high Energy Neutrino Detectors
• Neutrino Detection, Position Calibration and Marine Science with Acoustic Arrays in the Deep Sea
• Listening for ultra-heavy dark matter with underwater acoustic detectors