标题： 基于深度学习的快速偏心自旋对齐双黑洞波形生成 显示英文标题

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

摘要： 二元黑洞（BBH）的精确波形模板对于引力波（GW）的检测和精确参数估计至关重要。 虽然SEOBNRE能够为偏心BBH系统生成精确的时域波形，但其生成速度仍然是分析此类系统的关键瓶颈。 加速模板生成对于数据分析改进和从观测数据中提取有价值的信息至关重要。 我们提出了SEOBNRE_AIq5e2，这是一种基于人工智能的代理模型，旨在加速偏心、自旋对齐的BBH系统的波形生成。 SEOBNRE_AIq5e2在训练过程中结合了先进的自适应重采样技术，能够生成质量比高达5、偏心率低于0.2且自旋$|\chi_z|$高达0.6的偏心BBH波形。 它实现了每波形4.3毫秒的惊人生成速度，平均不匹配度为$1.02 \times 10^{-3}$。 凭借卓越的准确性和快速性能，SEOBNRE_AIq5e2成为未来分析偏心引力波数据的有前途的波形模板。 显示英文摘要

摘要： 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 $|\chi_z|$ up to 0.6. It achieves an impressive generation speed of 4.3 ms per waveform with a mean mismatch of $1.02 \times 10^{-3}$. With the exceptional accuracy and rapid performance, SEOBNRE_AIq5e2 emerges as a promising waveform template for future analysis of eccentric gravitational wave data.