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

Abstract

Recent developments in deep learning techniques have offered an alternative and complementary approach to traditional matched filtering methods for the identification of gravitational wave (GW) signals. The rapid and accurate identification of GW signals is crucial for the progress of GW physics and multi-messenger astronomy, particularly in light of the upcoming fourth and fifth observing runs of LIGO-Virgo-KAGRA. In this work, we use the 2D U-Net algorithm to identify the time-frequency domain GW signals from stellar-mass binary black hole (BBH) mergers. We simulate BBH mergers with component masses from 5 to 80 $M_{\odot}$ and account for the LIGO detector noise. We find that the GW events in the first and second observation runs could all be clearly and rapidly identified. For the third observation run, about $80\%$ GW events could be identified and GW190814 is inferred to be a BBH merger event. Moreover, since the U-Net algorithm has advantages in image processing, the time-frequency domain signals obtained through U-Net can preliminarily determine the masses of GW sources, which could help provide the mass priors for future parameter inferences. We conclude that the U-Net algorithm could rapidly identify the time-frequency domain GW signals from BBH mergers and provide great help for future parameter inferences.Comment: 11 pages, 9 figure