Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders
This work addresses gravitational wave detection for astrophysics research by offering a source-agnostic method, though it is incremental as it builds on existing anomaly detection techniques.
The paper tackles the problem of detecting gravitational wave signals in laser interferometers using unsupervised anomaly detection with recurrent autoencoders, achieving performance that outperforms other autoencoder architectures but with lower accuracy compared to supervised techniques.
We present an application of anomaly detection techniques based on deep recurrent autoencoders to the problem of detecting gravitational wave signals in laser interferometers. Trained on noise data, this class of algorithms could detect signals using an unsupervised strategy, i.e., without targeting a specific kind of source. We develop a custom architecture to analyze the data from two interferometers. We compare the obtained performance to that obtained with other autoencoder architectures and with a convolutional classifier. The unsupervised nature of the proposed strategy comes with a cost in terms of accuracy, when compared to more traditional supervised techniques. On the other hand, there is a qualitative gain in generalizing the experimental sensitivity beyond the ensemble of pre-computed signal templates. The recurrent autoencoder outperforms other autoencoders based on different architectures. The class of recurrent autoencoders presented in this paper could complement the search strategy employed for gravitational wave detection and extend the reach of the ongoing detection campaigns.