改进 PTA 数据分析中的贝叶斯推断：基于 Normalizing Flows 的重要性嵌套采样
Improving Bayesian inference in PTA data analysis: importance nested sampling with Normalizing Flows
AI总结：
本文提出在 Enterprise 框架中集成基于 normalizing flow 的 i-nessai 嵌套采样器，用于 PTA 数据的贝叶斯推断，在保证后验与证据准确性的同时，将运行时间最多缩短三个数量级。
摘要：
我们针对脉冲星计时阵列（PTA）数据，详细研究了其贝叶斯推断工作流，重点是通过使用基于 normalizing flow 的嵌套采样来提升效率、鲁棒性和速度。基于 Enterprise 框架，我们集成了 i-nessai 采样器，并在真实的模拟数据集上对其性能进行了基准测试。我们分析了它的计算扩展性和稳定性，结果表明，与传统的单核平行回火 MCMC 分析相比，它能实现准确的后验分布和可靠的证据估计，且根据数据集配置的不同，运行时间最多可减少三个数量级。这些结果突显了基于 flow 的嵌套采样在加速 PTA 分析的同时保持推断质量的潜力。
英文摘要：
We present a detailed study of Bayesian inference workflows for pulsar timing array data with a focus on enhancing efficiency, robustness and speed through the use of normalizing flow-based nested sampling. Building on the Enterprise framework, we integrate the i-nessai sampler and benchmark its performance on realistic, simulated datasets. We analyze its computational scaling and stability, and show that it achieves accurate posteriors and reliable evidence estimates with substantially reduced runtime, by up to three orders of magnitude depending on the dataset configuration, with respect to conventional single-core parallel-tempering MCMC analyses. These results highlight the potential of flow-based nested sampling to accelerate PTA analyses while preserving the quality of the inference.
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