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 AI-based surrogate model that 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 ∣χz∣ up to 0.6. It achieves an impressive generation speed of 4.3 ms per waveform with a mean mismatch of 1.02×10−3. With the exceptional accuracy and rapid performance, SEOBNRE_AIq5e2 emerges as a promising waveform template for future analysis of eccentric gravitational wave data.
@article{arxiv.2411.14893,
title = {Rapid eccentric spin-aligned binary black hole waveform generation based on deep learning},
author = {Ruijun Shi and Yue Zhou and Tianyu Zhao and Zhixiang Ren and Zhoujian Cao},
journal= {arXiv preprint arXiv:2411.14893},
year = {2024}
}