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arXiv · 2411.14893

Rapid eccentric spin-aligned binary black hole waveform generation based on deep learning

Ruijun Shi·Yue Zhou·Tianyu Zhao·Zhixiang Ren·Zhoujian Cao
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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 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 $|χ_z|$ up to 0.6. It achieves an impressive generation speed of 4.3 ms per waveform with a mean mismatch of $1.02 \times 10^{-3}$. With the exceptional accuracy and rapid performance, SEOBNRE_AIq5e2 emerges as a promising waveform template for future analysis of eccentric gravitational wave data.

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Ruijun Shi, Yue Zhou, Tianyu Zhao, Zhixiang Ren, Zhoujian Cao. 2024-11-22. Rapid eccentric spin-aligned binary black hole waveform generation based on deep learning. https://arxiv.org/abs/2411.14893

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