Real-time detection of gravitational waves from binary neutron stars using artificial neural networks

Abstract

The groundbreaking discoveries of gravitational waves from binary black-hole mergers [1][2][3] and, most recently, coalescing neutron stars [4] started a new era of Multi-Messenger Astrophysics and revolutionize our understanding of the Cosmos. Machine learning techniques such as artificial neural networks are already transforming many technological fields and have also proven successful in gravitational-wave astrophysics for detection and characterization of gravitational-wave signals from binary black holes [5][6][7]. Here we use a deep-learning approach to rapidly identify transient gravitational-wave signals from binary neutron star mergers in noisy time series representative of typical gravitational-wave detector data. Specifically, we show that a deep convolution neural network trained on 100,000 data samples can rapidly identify binary neutron star gravitational-wave signals and distinguish them from noise and signals from merging black hole binaries. These results demonstrate the potential of artificial neural networks for real-time detection of gravitational-wave signals from binary neutron star mergers, which is critical for a prompt follow-up and detailed observation of the electromagnetic and astro-particle counterparts accompanying these important transients.The detections of gravitational waves from binary black hole (BBH) mergers have verified Einstein's theory of General Relativity in extraordinary detail in the most violent astrophysical environments [1][2][3][4]8]. In addition, the first observation of coalescing neutron stars in both gravitational and electromagnetic spectra has initiated the era of Multi-Messenger Astrophysics, which uses observations in electromagnetic radiation, gravitational waves, cosmic rays, and neutrinos to provide deeper insights about properties of astrophysical objects and phenomena [4,9]. These discoveries were made possible by the Advanced Laser Interferometer Gravitational Wave Observatory (LIGO) and Virgo collaborations. As gravitational-wave detectors increase their sensitivity many more observations, including BBH, binary neutron star (BNS) and black hole -neutron star (BHNS) signals are likely to be detected more frequently. Conventional gravitational-wave detection techniques are based mainly on a method known as template matched filtering [7,10], which typically uses large banks of template waveforms each with different compact binary parameters, such as component masses and/or spins. Since parameters are not known in advance, a template bank spans a large astronomical parameter space, which makes these approaches very computationally expensive and challenging, especially for real-time detection of important gravitational wave transients where a rapid follow-up is critical for successful observation of their

Deep learning for core-collapse supernova detection

“…In addition, there are no limitations in the size of the templates bank of GW signals, and even more, it is preferable to use large datasets to cover as deep a parameter space as possible. Because of this fact they sparked the interest of several authors, who have built deep-learning algorithms to demonstrate their power on specific examples, including CCSN [15][16][17] among others [18,[32][33][34].…”

Confluence of Artificial Intelligence and High Performance Computing for Accelerated, Scalable and Reproducible Gravitational Wave Detection

“…spinning, non-precessing, binary systems [52]; have the same sensitivity as template matching algorithms; and are orders of magnitude faster, at a fraction of the computational cost. There is also a vigorous program to apply AI to accelerate the detection of binary neutron stars [40,41], and to forecast the merger of multi-messenger sources, such as binary neutron stars and neutron star-black hole systems [50,51].…”

“…Research and development in deep learning is moving at an incredible pace [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51]. Specific milestones in the development of AI tools for gravitational wave astrophysics include the construction of neural networks that describe the same 4-D signal manifold of established gravitational wave detection pipelines, i.e., the masses of the binary components and the z-component of the 3-D spin vector:…”

Early warning of coalescing neutron-star and neutron-star-black-hole binaries from the nonstationary noise background using neural networks

“…[38][39][40] showed that it is possible to input real-time GW readout and then use NNs to detect massive black hole binaries (BBHs) and later Refs. [41,42] considered the possibility of detecting BNSs with longer signal duration. Recently, Ref.…”