  With the advent of gravitational-wave astronomy and the discovery of more
compact binary coalescences, data quality improvement techniques are desired to
handle the complex and overwhelming noise in gravitational wave (GW)
observational data. Though recent machine learning-based studies have shown
promising results for data denoising, they are unable to precisely recover both
the GW signal amplitude and phase. To address such an issue, we develop a deep
neural network centered workflow, WaveFormer, for significant noise suppression
and signal recovery on observational data from the Laser Interferometer
Gravitational-Wave Observatory (LIGO). The WaveFormer has a science-driven
architecture design with hierarchical feature extraction across a broad
frequency spectrum. As a result, the overall noise and glitch are decreased by
more than one order of magnitude and the signal recovery error is roughly 1%
and 7% for the phase and amplitude, respectively. Moreover, on 75 reported
binary black hole (BBH) events of LIGO we obtain a significant improvement of
inverse false alarm rate. Our work highlights the potential of large neural
networks in gravitational wave data analysis and, while primarily demonstrated
on LIGO data, its adaptable design indicates promise for broader application
within the International Gravitational-Wave Observatories Network (IGWN) in
future observational runs.
