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We report on the construction of a deep convolutional neural network that canreproduce the sensitivity of a matched-filtering search for binary black holegravitational-wave signals. The standard method for the detection of wellmodeled transient gravitational-wave signals is matched filtering. However, thecomputational cost of such searches in low latency will grow dramatically asthe low frequency sensitivity of gravitational-wave detectors improves.Convolutional neural networks provide a highly computationally efficient methodfor signal identification in which the majority of calculations are performedprior to data taking during a training process. We use only whitened timeseries of measured gravitational-wave strain as an input, and we train and teston simulated binary black hole signals in synthetic Gaussian noiserepresentative of Advanced LIGO sensitivity. We show that our network canclassify signal from noise with a performance that emulates that of matchfiltering applied to the same datasets when considering the sensitivity definedby Reciever-Operator characteristics.

Matching matched filtering with deep networks in gravitational-wave astronomy