Deep learning model based on a bidirectional gated recurrent unit for the detection of gravitational wave signals

Abstract

The application of deep learning to the detection of gravitational wave (GW) signals has attracted much attention in recent years, and nearly all of the existing deep learning-based GW detection algorithms are based on the convolutional neural network (CNN), which can be used to identify the GW merger moment and produce responding triggers. However, current CNN-based models are not best optimized tools in modeling the causal logic of sequential data in the time dimension to incorporate the information of the inspiral, merger and ringdown phases of the GW waveform. To address this issue we propose a deep learning model designed based on the bidirectional gated recurrent unit (BiGRU), a variant of the recurrent neural network (RNN) that is sensitive to the sequential logic of GW data. We train the neural network and implement the GW detection task by using the data obtained during the first, second and third observation runs (O1/O2/O3) of the LIGO-VIGRO scientific collaboration. We find that our BiGRU model achieves a false alarm probability (FAP) of $4.88\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}4}$ (corresponding false alarm rate is about 1 per 18.2 hours) and maintains a detection ratio of about 89.6%. We also construct a model ensemble containing two independently trained BiGRU models and find it does not trigger any false alarm events on our testing dataset. Meanwhile, our BiGRU model not only recovers all the reported GW events of O1-O2 runs, including the unique (as of today) confident (with observed electromagnetic counterparts) GW event originating from a binary neutron star (BNS) merger (GW170817), but also achieves a detection ratio of about 80% of the O3 run. By calculating and comparing the receiver operator characteristic (ROC) curves we demonstrate the superiority of the BiGRU model over traditional CNN-based models, and show that the BiGRU model, being optimized to deal with GW waveform that sequentially contains the inspiral, merger and ringdown phases, is a potentially powerful tool to study the GW data in the future.