Accelerated Sequential Posterior Inference via Reuse for Gravitational-Wave Analyses
基于复用的引力波分析加速序列后验推断
We introduce accelerated sequential posterior inference via reuse (ASPIRE), a broadly applicable framework that transforms existing posterior samples and Bayesian evidence estimates into unbiased results under alternative models without rerunning the original analysis. ASPIRE combines normalizing flows with a generalized sequential Monte Carlo (SMC) scheme, enabling efficient updates of existing results and reducing total likelihood evaluations and wall times by factors of up to 5.8 and 5.5, respectively, with larger gains per posterior sample. This addresses a growing problem in gravitational-wave astronomy, where events must be repeatedly reanalyzed under different models or physical hypotheses. We show that ASPIRE reproduces full Bayesian results when switching waveform models or adding physical effects such as spin precession and orbital eccentricity. With this statistical robustness, ASPIRE turns repeated reanalyses into fast, reliable updates—paving the way for systematic studies of waveform systematics, scalable reanalyses across large event catalogs, and broadly applicable Bayesian reanalysis across other scientific domains.
展开 ▾首次实现从已有后验出发，在变更波形或引入自旋进动、轨道偏心率等新物理时仍获得无偏后验与证据，显著降低重复分析的计算代价，为波形系统误差研究和大规模重分析提供高效工具。
ASPIRE 突破了传统引力波数据分析中每次模型变更需重新运行的瓶颈。相较于重要性采样 Ashton 2025 和后验重划分 Prathaban+ 2026 等先前方法，该框架通过归一化流近似已有后验并结合自适应 SMC，确保在分布差异大或引入新参数时仍保持无偏性；与平行回火 MCMC Earl+ 2005 相比，其自动化的中间分布构建提供了更稳健的证据估计。未来，整合梯度提议 Buchholz+ 2021 和粒子回收 Karamanis+ 2025 等策略有望进一步提升采样效率，而 ASPIRE 的思路亦可能拓展至粒子物理 Gärtner+ 2024 和宇宙学 Arjona+ 2022 中的模型对比分析。
预印本 2025-11-06 · 刊出 2026-07-10 · 收录 2026-07-22