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Abstract

As two neutron stars merge, they emit gravitational waves that can
potentially be detected by earth bound detectors. Matched-filtering based
algorithms have traditionally been used to extract quiet signals embedded in
noise. We introduce a novel neural-network based machine learning algorithm
that uses time series strain data from gravitational-wave detectors to detect
signals from non-spinning binary neutron star mergers. For the Advanced LIGO
design sensitivity, our network has an average sensitive distance of 130 Mpc at
a false-alarm rate of 10 per month. Compared to other state-of-the-art machine
learning algorithms, we find an improvement by a factor of 6 in sensitivity to
signals with signal-to-noise ratio below 25. However, this approach is not yet
competitive with traditional matched-filtering based methods. A conservative
estimate indicates that our algorithm introduces on average 10.2 s of latency
between signal arrival and generating an alert. We give an exact description of
our testing procedure, which can not only be applied to machine learning based
algorithms but all other search algorithms as well. We thereby improve the
ability to compare machine learning and classical searches.