  The groundbreaking discoveries of gravitational waves from binary black-hole
mergers and, most recently, coalescing neutron stars 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. 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.
