发表机构
School of Astronomy and Space Science, University of Chinese Academy of Sciences (UCAS); School of Fundamental Physics and Mathematical Sciences, Hangzhou Institute for Advanced Study, UCAS; International Center for Theoretical Physics Asia-Pacific（中国科学院大学天文与空间科学学院; 杭州高等研究院基础物理与数学学院; 亚太理论物理中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究空间干涉仪中相邻测试质量加速度噪声相关性对随机引力波背景搜索的影响，发现忽略该相关性的模板会产生系统性参数偏移，而包含相关性的模板能更准确恢复参数。
AI 中文摘要
随机引力波背景（SGWB）是未来空间干涉仪的主要科学目标，因为它编码了未解析的天体物理群体和早期宇宙物理过程的信息。然而，其随机性意味着其探测和参数推断依赖于将其对数据的统计贡献与仪器噪声的贡献区分开来。特别是，SGWB和仪器噪声都对数据协方差有贡献，因此不完整的噪声模型可能被误认为是SGWB的一部分，从而偏置推断的频谱及其参数。在这里，我们研究了每艘航天器中相邻测试质量（TM）的加速度（ACC）噪声之间相关性存在时的这种效应。我们在单链路层面模拟相关噪声，通过移动轨道响应将其传播到时间延迟干涉测量（TDI）可观测量中，并使用两种贝叶斯频域模板分析所得的TDI自谱：相关模板包括相关噪声协方差，而非相关模板则忽略它。我们发现，当模拟数据中存在相关的ACC噪声时，非相关模板表现出系统性参数偏移，这些偏移通常随着相关幅度的增加而变得更加明显。相比之下，相关模板能显著更准确地恢复注入的SGWB和仪器噪声参数。这些结果表明，测试质量ACC噪声中的相关性可能构成空间SGWB搜索的相关系统效应，并激励在任务现实的噪声模型和推断流程中明确包含这些相关性。
英文摘要
The stochastic gravitational-wave background (SGWB) is a major science target of future space-based interferometers because it encodes information about unresolved astrophysical populations and physical processes in the early Universe. Its stochastic nature, however, means that its detection and parameter inference rely on distinguishing its statistical contribution to the data from that of instrumental noise. In particular, both the SGWB and instrumental noise contribute to the data covariance, so an incomplete noise model can be misinterpreted as part of the SGWB and thereby bias the inferred spectrum and its parameters. Here we investigate this effect in the presence of correlations between the acceleration (ACC) noises of adjacent test masses (TMs) in each spacecraft. We simulate the correlated noise at the single-link level, propagate it through the moving-orbit response into the time-delay interferometry (TDI) observables, and analyze the resulting TDI auto spectra with two Bayesian frequency-domain templates: the Corr. Template includes the correlated-noise covariance, whereas the Non-Corr. Template neglects it. We find that, when correlated ACC noise is present in the mock data, the Non-Corr. Template exhibits systematic parameter offsets that generally become more pronounced with increasing correlation amplitude. In contrast, the Corr. Template recovers the injected SGWB and instrumental-noise parameters substantially more accurately. These results demonstrate that correlations in test-mass ACC noise can constitute a relevant systematic for space-based SGWB searches and motivate their explicit inclusion in mission-realistic noise models and inference pipelines.
Comments30 pages, 8 figures
