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The groundbreaking discoveries of gravitational waves from binary black-holemergers and, most recently, coalescing neutron stars started a new era ofMulti-Messenger Astrophysics and revolutionized our understanding of theCosmos. Machine learning techniques such as artificial neural networks arealready transforming many technological fields and have also proven successfulin gravitational-wave astrophysics for detection and characterization ofgravitational-wave signals from binary black holes. Here we use a deep-learningapproach to rapidly identify transient gravitational-wave signals from binaryneutron star mergers in noisy time series representative of typicalgravitational-wave detector data. Specifically, we show that a deep convolutionneural network trained on 100,000 data samples can rapidly identify binaryneutron star gravitational-wave signals and distinguish them from noise andsignals from merging black hole binaries. These results demonstrate thepotential of artificial neural networks for real-time detection ofgravitational-wave signals from binary neutron star mergers, which is criticalfor a prompt follow-up and detailed observation of the electromagnetic andastro-particle counterparts accompanying these important transients.

Real-Time Detection of Gravitational Waves from Binary Neutron Stars using Artificial Neural Networks