修正随机引力波背景模板化搜索中的证据不确定性
Correcting for Evidence Uncertainty in the Templated Background Search for the Stochastic Gravitational-Wave Background
- University of Minnesota, School of Physics and Astronomy（明尼苏达大学物理与天文学院）
- Center for Gravitational Physics, University of Texas at Austin（德克萨斯大学奥斯汀分校引力物理中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究揭示了贝叶斯模板化背景搜索中因嵌套采样证据不确定性导致的系统性偏差，并提出一种简单有效的修正方法，同时强调搜索先验设计对稳健推断的重要性。
AI中文摘要：
我们报告了在针对天体物理随机引力波背景的贝叶斯模板化背景搜索（TBS）中一个系统性偏差的新来源。该偏差源于分段水平上嵌套采样证据估计的统计不确定性，并且即使似然模型准确描述了数据，这种偏差仍然存在。利用一个解析上可处理的模型以及一个基于天体物理动机的双黑洞种群的频域模拟数据分析，我们表明当累积数百万个分段时，这种不确定性会相干地使推断的占空比膨胀高达一个数量级。我们提出了一种简单而高效的修正方法，并在所有测试配置中证明了其有效性。我们还发现，将搜索先验限制在高最优信噪比（SNR）下会错误地将中等强度信号归因，而将先验扩展到支持较弱信号则会放大证据不确定性。随着TBS分析扩展到即将到来的观测运行，修正和仔细的搜索先验设计对于稳健推断都至关重要。
英文摘要：
We report a new source of systematic bias in the Bayesian Templated Background Search (TBS) for the astrophysical stochastic gravitational-wave background. This bias arises from the statistical uncertainty in nested sampling evidence estimates at the segment level and persists even when the likelihood model accurately characterizes the data. Using an analytically tractable model and a frequency-domain mock data analysis with an astrophysically motivated binary black hole population, we show that this uncertainty coherently inflates the inferred duty cycle by up to an order of magnitude when millions of segments are accumulated. We propose a simple and efficient correction and demonstrate its effectiveness across all tested configurations. We also find that a search prior restricted to high optimal signal-to-noise ratio (SNR) misattributes intermediate-strength signals, while extending the prior to support weaker signals amplifies the evidence uncertainty. Both the correction and careful search prior design are essential for robust inference as TBS analyses scale to upcoming observing runs.
