With a laser interferometric gravitational-wave detector in separate free flying spacecraft, the only way to achieve detection is to mitigate the dominant noise arising from the frequency fluctuations of the lasers via postprocessing. The noise can be effectively filtered out on the ground through a specific technique called time-delay interferometry (TDI), which relies on the measurements of time delays between spacecraft and careful modeling of how laser noise enters the interferometric data. Recently, this technique has been recast into a matrix-based formalism by several authors, offering a different perspective on TDI, particularly by relating it to principal component analysis (PCA). In this work, we demonstrate that we can cancel laser frequency noise by directly applying PCA to a set of shifted data samples, without any prior knowledge of the relationship between single-link measurements and noise, nor time delays. We show that this fully data-driven algorithm achieves a gravitational-wave sensitivity similar to classic TDI.

中文翻译：

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基于主成分分析的模型无关时延干涉测量法

在单独的自由飞行航天器中使用激光干涉引力波探测器，实现探测的唯一方法是通过后处理减轻由激光器频率波动引起的主要噪声。通过称为延时干涉测量 (TDI) 的特定技术，可以在地面上有效滤除噪声，该技术依赖于航天器之间时间延迟的测量以及对激光噪声如何进入干涉测量数据的仔细建模。最近，该技术已被几位作者改写为基于矩阵的形式，为 TDI 提供了不同的视角，特别是通过将其与主成分分析 (PCA) 相关联。在这项工作中，我们证明我们可以通过将 PCA 直接应用于一组移位数据样本来消除激光频率噪声，没有任何关于单链路测量和噪声之间关系的先验知识，也没有时间延迟。我们表明，这种完全由数据驱动的算法实现了类似于经典 TDI 的引力波灵敏度。