Deep multi-view models for glitch classification

Sara Bahaadini; Neda Rohani; Scott Coughlin; Michael Zevin; Vicky Kalogera; Aggelos K. Katsaggelos

ICASSP 2017accepted0 citations

Deep multi-view models for glitch classification

Sara Bahaadini, Neda Rohani, Scott Coughlin, Michael Zevin, Vicky Kalogera, Aggelos K. Katsaggelos

Abstract

Non-cosmic, non-Gaussian disturbances known as “glitches”, show up in gravitational-wave data of the Advanced Laser Interferometer Gravitational-wave Observatory, or aLIGO. In this paper, we propose a deep multi-view convolutional neural network to classify glitches automatically. The primary purpose of classifying glitches is to understand their characteristics and origin, which facilitates their removal from the data or from the detector entirely. We visualize glitches as spectrograms and leverage the state-of-the-art image classification techniques in our model. The suggested classifier is a multi-view deep neural network that exploits four different views for classification. The experimental results demonstrate that the proposed model improves the overall accuracy of the classification compared to traditional single view algorithms.

BibTeX

@inproceedings{icassp2017_deepmultiviewmod,
  title = {Deep multi-view models for glitch classification},
  author = {Sara Bahaadini and Neda Rohani and Scott Coughlin and Michael Zevin and Vicky Kalogera and Aggelos K. Katsaggelos},
  booktitle = {ICASSP 2017},
  year = {2017}
}