WaveFormer: transformer-based denoising method for gravitational-wave data
He Wang,
Yue Zhou,
Zhoujian Cao
et al.
Abstract: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 signifi… Show more
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