  The pre-merger detection of gravitational waves from the early inspiral phase
of compact binary coalescence events would allow the observation of the earlier
stages of the merger in the electromagnetic band. This would significantly
impact multi-messenger astronomy, giving astronomers potential access to rich
new information. Here, we introduce a proof-of-concept deep-learning-based
approach to produce pre-merger early-warning alerts for binary black hole
systems. We show the possibility of using a Long Short-Term Memory network
trained on the whitened detector strain in the time domain to detect and
classify compact binary events. In this work, we consider a single advanced
Laser Interferometer Gravitational-Wave Observatory detector at design
sensitivity and make approximate sensitivity and early warning capability
comparisons with approximations to traditional matched filtering approaches. We
find that our model is competitive in both aspects, and when applied to a
simulated test dataset was able to produce an early alert up to four seconds
before the merger.
