用流模型加速脉冲星定时阵列贝叶斯推断，提速百倍以上。
Improving Bayesian inference in PTA data analysis: importance nested sampling with Normalizing Flows
- 基于流模型的嵌套采样，提升采样效率与稳定性。
- 相比传统方法，运行时间缩短达三个数量级。
- 适合需要快速高精度推断的引力波探测研究者。
我们针对脉冲星定时阵列数据的贝叶斯推断工作流程进行了深入研究，重点通过基于归一化流的嵌套采样提升效率、鲁棒性和速度。在Enterprise框架基础上，集成i-nessai采样器，并在真实模拟数据集上进行性能基准测试。分析其计算复杂度与稳定性，结果表明，该方法在不同数据配置下可实现准确的后验分布和可靠的证据估计，运行时间相比传统的单核并行退火MCMC分析最多减少三个数量级。这些结果凸显了基于流的嵌套采样在加速脉冲星定时阵列分析方面的潜力，同时保持推断质量。
原文摘要 · Abstract (English)
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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