Ensemble of deep convolutional neural networks for real-time gravitational wave signal recognition

With the rapid development of deep learning technology, more and more researchers apply it to gravitational wave (GW) data analysis. Previous studies focused on a single deep learning model. In this paper we design an ensemble algorithm combining a set of convolutional neural networks for GW signal recognition. The whole ensemble model consists of two subensemble models. Each subensemble model is also an ensemble model of deep learning. The two subensemble models treat data of Hanford and Livinston detectors, respectively. Proper voting scheme is adopted to combine the two subensemble models to form the whole ensemble model. We apply this ensemble model to all reported GW events in the first observation and second observation runs (O1/O2) by LIGO-VIRGO Scientific Collaboration. We find that the ensemble algorithm can clearly identify all binary black hole merger events except GW170818. We also apply the ensemble model to one month (August 2017) data of O2. No false trigger happens, although only O1 data are used for training. Our test results indicate that the ensemble learning algorithms can be used in real-time GW data analysis.