Deep Multi-view Models for Glitch Classification
This work addresses the need for better glitch classification to aid in data cleaning for gravitational-wave astronomy, but it is incremental as it builds on existing image classification techniques.
The paper tackles the problem of automatically classifying glitches in gravitational-wave data from aLIGO using a deep multi-view convolutional neural network, resulting in improved overall accuracy compared to traditional single-view algorithms.
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.