LISA-Taiji-TianQin 网络的相关银河系混淆前景及其对可分辨源分析的影响
Correlated Galactic Confusion Foreground for the LISA-Taiji-TianQin Network and Its Impacts on Resolvable-Source Analysis
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中文总结 AI 辅助
针对 LISA-Taiji-TianQin 网络，构建相关银河系混淆前景噪声模型，发现其显著影响高质量 MBHB 和低频 GB 的 SNR，但无参数偏差，并公开代码与数据。
中文摘要 AI 辅助
毫赫兹引力波天空预计将在 2030 年代由一组天基探测器（LISA、Taiji 和 TianQin）组成的网络进行观测。该频段内的一个主要噪声是由约 O(10^7) 个不可分辨的银河系双星（GBs）产生的混淆前景。由于同一双星种群投影到不同探测器的时间延迟干涉（TDI）通道上，该前景在网络中必然是相关的。我们通过数值模拟，利用约 3×10^7 个 GBs 的星表构建了 LISA-Taiji-TianQin 网络的完整前景噪声协方差矩阵，并推导了跨探测器前景相干性的解析模型，该模型为数值结果提供了交叉验证和物理解释。我们基于该协方差矩阵推导了探测器网络的整体灵敏度，并进一步刻画了频率和时间相关的跨探测器前景相关性。以超大质量黑洞双星（MBHBs）和 GBs 作为代表性的瞬态源和连续源，我们进一步比较了块对角（即忽略跨探测器前景相关性）和全协方差噪声模型对信噪比（SNR）、参数不确定性和贝叶斯后验的影响。该影响局限于特定的信号区域，主要影响高质量（M_c ~ 10^7 M_sun）MBHBs 和低频（f_0 <~ 2 mHz）GBs，其 SNR 相对差异分别高达约 30% 和约 10%。在任一噪声模型下均未出现统计显著的参数偏差，这已通过概率-概率检验得到验证。MBHBs 和 GBs 的参数估计使用 Triangle-BBH 和 Triangle-GB 代码中实现的网络分析流程进行。这两个代码库，连同模拟的前景数据和网络灵敏度，均已公开发布，以供多样化的科学研究使用。
英文摘要
The millihertz gravitational-wave sky is expected to be observed by a network of space-based detectors (LISA, Taiji, and TianQin) in the 2030s. A dominant noise in this band is the confusion foreground produced by O(10^7) unresolved Galactic binaries (GBs). Because the same population projects onto the time-delay interferometry (TDI) channels of the different detectors, the foreground is necessarily correlated across the network. We construct the full foreground noise covariance matrix for the LISA-Taiji-TianQin network from a catalogue of ~3 x 10^7 GBs through numerical simulation, and we derive an analytic model of the cross-detector foreground coherence that provides a cross-check and physical interpretation of the numerical results. We derive the overall sensitivity of the detector network based on this covariance matrix, and further characterize the frequency- and time-dependent cross-detector foreground correlations. Taking massive black hole binaries (MBHBs) and GBs as representative transient and continuous sources, we further compare the block-diagonal (i.e., neglecting the cross-detector foreground correlation) and full-covariance noise models in terms of their impacts on the signal-to-noise ratio (SNR), parameter uncertainties, and Bayesian posteriors. The impact is confined to specific signal regimes, affecting primarily high-mass (M_c ~ 10^7 M_sun) MBHBs and low-frequency (f_0 <~ 2 mHz) GBs, with SNR relative differences of up to ~30% and ~10%, respectively. No statistically significant parameter bias arises under either noise model, as verified by probability-probability tests. Parameter estimations for MBHBs and GBs are performed using the network analysis pipelines implemented in the Triangle-BBH and Triangle-GB codes. Both repositories, together with the simulated foreground data and network sensitivities, are publicly released for diverse scientific investigations.
发表机构
- Institute of Mechanics, Chinese Academy of Sciences（中国科学院力学研究所）
- University of Chinese Academy of Sciences (UCAS)（中国科学院大学）
- Hangzhou Institute for Advanced Study, UCAS（杭州高等研究院，中国科学院大学）
- Lanzhou University（兰州大学）
机构由 AI 辅助整理，请以论文原文为准。
