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.

翻译：我们提出了一种基于高性能计算技术与归一化流的加速流程，用于联合贝叶斯参数估计与模型选择，并展示了其在引力波天体物理学中的高效性。我们将Jim推断工具包（一种归一化流增强的马尔可夫链蒙特卡洛采样器）与学习调和平均估计器相结合。我们的贝叶斯证据估计在单GPU上运行的结果，与传统嵌套采样技术在16个CPU核心上运行的结果一致，同时在4维和11维引力波推断问题上分别将计算时间减少了5倍和15倍。我们的代码已在经过充分测试和详细记录的开源软件包中提供，确保广大研究社区的可访问性与可复现性。