Rapid eccentric spin-aligned binary black hole waveform generation based on deep learning
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Abstract
Accurate waveform templates of binary black holes (BBHs) with eccentric orbits are essential for the detection and precise parameter estimation of gravitational waves (GWs). While seobnre produces accurate time-domain waveforms for eccentric BBH systems, its generation speed remains a critical bottleneck in analyzing such systems. Accelerating template generation is crucial to data analysis improvement and valuable information extraction from observational data. We present seobnre_aiq5e2, an innovative artificial intelligence-based surrogate model that was crafted to accelerate waveform generation for eccentric, spin-aligned BBH systems. seobnre_aiq5e2 incorporates an advanced adaptive resampling technique during training, enabling the generation of eccentric BBH waveforms with mass ratios up to 5, eccentricities below 0.2, and spins $|{\ensuremath{\chi}}_{z}|$ up to 0.6. It achieves an impressive generation speed of 4.3 ms per waveform with a mean mismatch of $1.02\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}3}$. With the exceptional accuracy and rapid performance, seobnre_aiq5e2 emerges as a promising waveform template for future analysis of eccentric gravitational wave data.
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