Source-agnostic gravitational-wave detection with recurrent autoencoders

Abstract

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.

Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers

“…The development of these AI models required training datasets with a few tens of thousands of modeled signals, and a couple of inexpensive GPUs to complete the training within a few hours. These findings sparked the interest of the gravitational wave community, leading to the organic creation of a vibrant, international community of researchers who are harnessing advances in AI and computing to address timely and pressing challenges in gravitational wave astrophysics [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. These seminal ideas have been applied for signal detection [28][29][30] and forecasting [31][32][33] of binary neutron stars, and for the detection and forecasting of neutron star-black hole systems [31,34].…”

Towards a robust and reliable deep learning approach for detection of compact binary mergers in gravitational wave data

“…In the past few years, applications of machine learning algorithms have been explored for a variety of tasks in GW data analysis, most notably, CBC searches [34][35][36][37], sensitivity improvements of existing search pipelines [38][39][40][41][42][43], non-linear noise modelling and subtraction [44,45] that is useful for early alerts for CBCs [46], parameter estimation [47,48], glitch classification [49][50][51][52][53][54][55], construction of population models [56][57][58][59][60][61], approximate GW detectability [62][63][64][65] and searches for other interesting signals originating from intermediate-mass black hole (IMBH) mergers [66], strongly-lensed events [67], short gamma-ray bursts [43], continuous waves [68] and unknown astrophysical events [69,70].…”

Physics-inspired spatiotemporal-graph AI ensemble for gravitational wave detection

“…The development of these AI models required training datasets with a few tens of thousands of modeled signals, and a couple of inexpensive GPUs to complete the training within a few hours. These findings sparked the interest of the gravitational wave community, leading to the organic creation of a vibrant, international community of researchers who are harnessing advances in AI and computing to address timely and pressing challenges in gravitational wave astrophysics [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] . These seminal ideas have been applied for signal detection [19][20][21] and forecasting [22][23][24] of binary neutron stars, and for the detection and forecasting of neutron star-black hole systems 22,25 .…”