Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning

Recent developments in deep learning techniques have provided alternative and complementary approaches to the traditional matched-filtering methods for identifying gravitational wave (GW) signals. The rapid and accurate identification of GW signals is crucial to the advancement of GW physics and multi-messenger astronomy, particularly considering the upcoming fourth and fifth observing runs of LIGO-Virgo-KAGRA. In this study, we used the 2D U-Net algorithm to identify time-frequency domain GW signals from stellar-mass binary black hole (BBH) mergers. We simulated BBH mergers with component masses ranging from 7 to 50 and accounted for the LIGO detector noise. We found that the GW events in the first and second observation runs could all be clearly and rapidly identified. For the third observing run, approximately 80% of the GW events could be identified. In contrast to traditional convolutional neural networks, the U-Net algorithm can output time-frequency domain signal images corresponding to probabilities, providing a more intuitive analysis. In conclusion, the U-Net algorithm can rapidly identify the time-frequency domain GW signals from BBH mergers.