Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning
arxiv(2023)
摘要
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_⊙ 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 observing run, about 80% GW events could
be identified. In particular, GW190814, currently unknown, is a special case
that can be identified by the network, while other binary neutron star mergers
and neutron star-black hole mergers can not be identified. Compared to the
traditional convolutional neural network, the U-Net algorithm can output the
time-frequency domain signal images rather than probabilities, providing a more
intuitive investigation. Moreover, some of the results through U-Net can
provide preliminary inference on the chirp mass information. In conclusion, the
U-Net algorithm can rapidly identify the time-frequency domain GW signals from
BBH mergers and potentially be helpful for future parameter inferences.
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