Abstract
The groundbreaking discoveries of gravitational waves from binary black-hole mergers [1–3] and, most recently, coalescing neutron stars [4] started a new era of Multi-Messenger Astrophysics and revolutionized 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 [5–7]. 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 promptly 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.
Highlights
The detections of gravitational waves (GWs) 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, 8]
Each neuron gives the inferred probability that the input time series belong to the noise, BBH signal, or binary neutron star (BNS) signal class, respectively
The optimal signalto-noise ratio (SNR) was varied from 1 to 20 in integer steps of 1 and the classifier was applied to time-series inputs containing approximately equal fractions of each class (Noise, BBH Signal, BNS Signal)
Summary
The detections of gravitational waves (GWs) 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, 8]. The first observation of coalescing neutron stars in both gravitational and electromagnetic spectra has initiated the era of Multi-Messenger Astrophysics (MMA), 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. 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. As it has been already pointed out in the literature
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