发表机构
University of Rhode Island; Institute for AI & Computational Research; California Institute of Technology; LIGO Laboratory（罗德岛大学; 人工智能与计算研究所; 加州理工学院; LIGO实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出用于模拟进动双黑洞合并的NRSur7dq4引力波形模型的神经网络替代模型，将波形分解后训练MLP，经验证精度高，在GPU上速度快且可微，结合了数值相对论精度与可微推理管道，适合相关应用及基于梯度的推理方法。
AI 中文摘要
我们提出了一种神经网络替代模型，用于模拟进动双黑洞合并的NRSur7dq4引力波形模型。该替代模型将波形分解为组成量，并为每个量训练一个独立的多层感知器（MLP）。我们在跨越其全参数空间（1≤q≤4，|χA,B|≤0.8）的10000个波形上针对NRSur7dq4验证了该替代模型。对于60至300M⊙之间的代表性总质量，中位数天空平均频域失配范围为8.0×10−5至1.7×10−4，第95百分位数低于10−3。在NVIDIA L40S GPU上，JAX替代模型端到端评估单个波形约需1毫秒，比NRSur7dq4的LALSimulation C实现快约10倍，在批量大小为64时维持约140倍的LALSimulation吞吐量，非常适合低延迟参数估计采样器和大规模波形生成。完整的NRSur7dq4 NN波形到似然管道在JAX中实现且可微。这是首个进动数值相对论波形模型的神经网络替代模型，将经过验证的与数值相对论忠实匹配的精度与完全可微、GPU加速的推理管道相结合，通过自动微分实现基于梯度的推理方法，包括费舍尔信息矩阵、GPU加速的嵌套采样、基于梯度的MCMC和重要性采样。
英文摘要
We present a neural network surrogate model that emulates the NRSur7dq4 gravitational waveform model for precessing binary black hole mergers. The surrogate decomposes the waveform into constituent quantities and trains an independent multilayer perceptron (MLP) for each. We validate the surrogate against NRSur7dq4 on 10,000 waveforms spanning its full parameter space ($1 \leq q \leq 4$, $|χ_{A,B}| \leq 0.8$). For representative total masses between 60 and 300 $M_\odot$, median sky-averaged frequency-domain mismatches range from $8.0 \times 10^{-5}$ to $1.7 \times 10^{-4}$, with 95th percentiles below $10^{-3}$. On an NVIDIA L40S GPU the JAX surrogate evaluates a single waveform in about 1 ms end-to-end, roughly 10 times faster than the LALSimulation C implementation of NRSur7dq4, and sustains about 140 times the LALSimulation throughput at batch size 64, making it well suited for both low-latency parameter-estimation samplers and large-scale waveform generation. The full NRSur7dq4 NN waveform-to-likelihood pipeline is implemented in JAX and is differentiable. This is the first neural-network surrogate of a precessing numerical-relativity waveform model to combine validated NR-faithful accuracy with a fully differentiable, GPU-accelerated inference pipeline, enabling gradient-based inference approaches via automatic differentiation including Fisher information matrices, GPU-accelerated nested sampling, gradient-based MCMC and importance sampling.
Comments37 pages, 18 figures
