  We report on the construction of a deep convolutional neural network that can
reproduce the sensitivity of a matched-filtering search for binary black hole
gravitational-wave signals. The standard method for the detection of well
modeled transient gravitational-wave signals is matched filtering. However, the
computational cost of such searches in low latency will grow dramatically as
the low frequency sensitivity of gravitational-wave detectors improves.
Convolutional neural networks provide a highly computationally efficient method
for signal identification in which the majority of calculations are performed
prior to data taking during a training process. We use only whitened time
series of measured gravitational-wave strain as an input, and we train and test
on simulated binary black hole signals in synthetic Gaussian noise
representative of Advanced LIGO sensitivity. We show that our network can
classify signal from noise with a performance that emulates that of match
filtering applied to the same datasets when considering the sensitivity defined
by Reciever-Operator characteristics.
