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Gravitational waves from the coalescence of compact-binary sources are nowroutinely observed by Earth bound detectors. The most sensitive searchalgorithms convolve many different pre-calculated gravitational waveforms withthe detector data and look for coincident matches between different detectors.Machine learning is being explored as an alternative approach to building asearch algorithm that has the prospect to reduce computational costs and targetmore complex signals. In this work we construct a two-detector search forgravitational waves from binary black hole mergers using neural networkstrained on non-spinning binary black hole data from a single detector. Thenetwork is applied to the data from both observatories independently and wecheck for events coincident in time between the two. This enables the efficientanalysis of large quantities of background data by time-shifting theindependent detector data. We find that while for a single detector the networkretains $91.5\%$ of the sensitivity matched filtering can achieve, this numberdrops to $83.9\%$ for two observatories. To enable the network to check forsignal consistency in the detectors, we then construct a set of simple networksthat operate directly on data from both detectors. We find that none of thesesimple two-detector networks are capable of improving the sensitivity overapplying networks individually to the data from the detectors and searching fortime coincidences.

From One to Many: A Deep Learning Coincident Gravitational-Wave Search