用流模型加速引力波参数估计与模型选择，效率提升5到15倍。
Accelerated Bayesian parameter estimation and model selection for gravitational waves with normalizing flows
- 结合流模型与马尔可夫链蒙特卡洛，构建高效贝叶斯推断流水线。
- 4维和11维问题分别提速5倍和15倍，单块GPU性能媲美16核CPU。
- 代码开源可复现，适合引力波数据处理与贝叶斯计算研究者。
我们提出一种基于高性能计算技术和归一化流的加速流水线，用于联合贝叶斯参数估计与模型选择，并在引力波天体物理中展示了其高效性。将Jim推断工具包（一种增强型归一化流马尔可夫链蒙特卡洛采样器）与学习型调和均值估计器集成。我们的贝叶斯证据估计在1块GPU上运行的结果，与传统嵌套采样技术在16个CPU核心上的结果一致，同时使4维和11维引力波推断问题的计算时间分别减少了5倍和15倍。代码以经过充分测试和详细文档化的开源包形式发布，确保了更广泛研究社区的可访问性和可复现性。
原文摘要 · Abstract (English)
We present an accelerated pipeline, based on high-performance computing techniques and normalizing flows, for joint Bayesian parameter estimation and model selection and demonstrate its efficiency in gravitational wave astrophysics. We integrate the Jim inference toolkit, a normalizing flow-enhanced Markov chain Monte Carlo (MCMC) sampler, with the learned harmonic mean estimator. Our Bayesian evidence estimates run on $1$ GPU are consistent with traditional nested sampling techniques run on $16$ CPU cores, while reducing the computation time by factors of $5\times$ and $15\times$ for $4$-dimensional and $11$-dimensional gravitational wave inference problems, respectively. Our code is available in well-tested and thoroughly documented open-source packages, ensuring accessibility and reproducibility for the wider research community.
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