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Abstract

We present the results of the first Machine Learning Gravitational-Wave
Search Mock Data Challenge (MLGWSC-1). For this challenge, participating groups
had to identify gravitational-wave signals from binary black hole mergers of
increasing complexity and duration embedded in progressively more realistic
noise. The final of the 4 provided datasets contained real noise from the O3a
observing run and signals up to a duration of 20 seconds with the inclusion of
precession effects and higher order modes. We present the average sensitivity
distance and runtime for the 6 entered algorithms derived from 1 month of test
data unknown to the participants prior to submission. Of these, 4 are machine
learning algorithms. We find that the best machine learning based algorithms
are able to achieve up to 95% of the sensitive distance of matched-filtering
based production analyses for simulated Gaussian noise at a false-alarm rate
(FAR) of one per month. In contrast, for real noise, the leading machine
learning search achieved 70%. For higher FARs the differences in sensitive
distance shrink to the point where select machine learning submissions
outperform traditional search algorithms at FARs $\geq 200$ per month on some
datasets. Our results show that current machine learning search algorithms may
already be sensitive enough in limited parameter regions to be useful for some
production settings. To improve the state-of-the-art, machine learning
algorithms need to reduce the false-alarm rates at which they are capable of
detecting signals and extend their validity to regions of parameter space where
modeled searches are computationally expensive to run. Based on our findings we
compile a list of research areas that we believe are the most important to
elevate machine learning searches to an invaluable tool in gravitational-wave
signal detection.