  The Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo
Interferometer Collaborations have now detected all three classes of compact
binary mergers: binary black hole (BBH), binary neutron star (BNS), and neutron
star-black hole (NSBH). For coalescences involving neutron stars, the
simultaneous observation of gravitational and electromagnetic radiation
produced by an event, has broader potential to enhance our understanding of
these events, and also to probe the equation of state (EOS) of dense matter.
However, electromagnetic follow-up to gravitational wave (GW) events requires
rapid real-time detection and classification of GW signals, and conventional
detection approaches are computationally prohibitive for the anticipated rate
of detection of next-generation GW detectors. In this work, we present the
first deep learning based results of classification of GW signals from NSBH
mergers in \textit{real} LIGO data. We show for the first time that a deep
neural network can successfully distinguish all three classes of compact binary
mergers and separate them from detector noise. Specifically, we train a
convolutional neural network (CNN) on $\sim 500,000$ data samples of real LIGO
noise with injected BBH, BNS, and NSBH GW signals, and we show that our network
has high sensitivity and accuracy. Most importantly, we successfully recover
the two confirmed NSBH events to-date (GW200105 and GW200115) and the two
confirmed BNS mergers to-date (GW170817 and GW190425), together with $\approx
90\%$ of all BBH candidate events from the third Gravitational Wave Transient
Catalog, GWTC-3. These results are an important step towards low-latency
real-time GW detection, enabling multi-messenger astronomy.
