论文标题
用于重力波检测时间延迟干涉法的统计推断方法
A statistical inference approach to time-delay interferometry for gravitational-wave detection
论文作者
论文摘要
未来的基于空间的引力波观测液LISA将由三角形星座中的三个航天器组成，该星座由用250万公里的激光干涉仪连接。除其他挑战外，任务的成功在很大程度上取决于激光频率噪声的取消质量，其功率在重力信号以上的八个数量级。进行噪声去除的标准技术是时间延迟干涉法（TDI）。 TDI构造了为取消激光噪声项而定制的延迟假仪测量值的线性组合。先前的工作证明了TDI与主成分分析（PCA）之间的关系。我们基于这个想法，以基于模型的可能性直接取决于人类表测量的模型可能性来开发TDI的扩展。假设固定的高斯噪声，我们在频域中使用PCA分解了测量协方差。我们获得了一个全面而紧凑的框架，我们称为“主成分干涉法”的PCI，并表明它提供了丽莎数据分析问题的最佳描述。
The future space-based gravitational wave observatory LISA will consist of a constellation of three spacecraft in a triangular constellation, connected by laser interferometers with 2.5 million-kilometer arms. Among other challenges, the success of the mission strongly depends on the quality of the cancellation of laser frequency noise, whose power lies eight orders of magnitude above the gravitational signal. The standard technique to perform noise removal is time-delay interferometry (TDI). TDI constructs linear combinations of delayed phasemeter measurements tailored to cancel laser noise terms. Previous work has demonstrated the relationship between TDI and principal component analysis (PCA). We build on this idea to develop an extension of TDI based on a model likelihood that directly depends on the phasemeter measurements. Assuming stationary Gaussian noise, we decompose the measurement covariance using PCA in the frequency domain. We obtain a comprehensive and compact framework that we call PCI for "principal component interferometry," and show that it provides an optimal description of the LISA data analysis problem.
