Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy
Published in Physical Review Letters, 2018
Recommended citation: Gabbard, Hunter, Michael Williams, Fergus Hayes, and Chris Messenger. “Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy.” Physical Review Letters 120, no. 14 (June 2018). https://doi.org/10.1103/physrevlett.120.141103. https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.141103
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. 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 data sets when considering the sensitivity defined by receiver-operator characteristics.
Recommended citation: Gabbard, Hunter, Michael Williams, Fergus Hayes, and Chris Messenger. “Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy.” Physical Review Letters 120, no. 14 (June 2018). https://doi.org/10.1103/physrevlett.120.141103.