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In this paper, we study an application of deep learning to the advanced LIGOand advanced Virgo coincident detection of gravitational waves (GWs) fromcompact binary star mergers. This deep learning method is an extension of theDeep Filtering method used by George and Huerta (2017) for multi-inputs ofnetwork detectors. Simulated coincident time series data sets in advanced LIGOand advanced Virgo detectors are analyzed for estimating source luminositydistance and sky location. As a classifier, our deep neural network (DNN) caneffectively recognize the presence of GW signals when the optimalsignal-to-noise ratio (SNR) of network detectors $\geq$ 9. As a predictor, itcan also effectively estimate the corresponding source space parameters,including the luminosity distance $D$, right ascension $\alpha$, anddeclination $\delta$ of the compact binary star mergers. When the SNR of thenetwork detectors is greater than 8, their relative errors are all less than23%. Our results demonstrate that Deep Filtering can process coincident GW timeseries inputs and perform effective classification and multiple space parameterestimation. Furthermore, we compare the results obtained from one, two, andthree network detectors; these results reveal that a larger number of networkdetectors results in a better source location.

science china. physics, mechanics and astronomy/science china. physics, mechanics and astronomy

Applying deep neural networks to the detection and space parameter  estimation of compact binary coalescence with a network of gravitational wave  detectors