Signal Classification for Acoustic Neutrino Detection

Details Signal Classification for Acoustic Neutrino Detection Signal Classification for Acoustic Neutrino Detection

2011-04-16

arxiv.org/abs/1104.3248v1

Astronomy and Astrophysics

Astrophysics

Instrumentation and Methods for Astrophysics

8 Pages, 6 Figures, ARENA 2010 Conference Proceedings

Scientific paper

10.1016/j.nima.2010.11.016

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.

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