A statistical inference approach to time-delay interferometry for gravitational-wave detection

TL;DR

This paper introduces PCI, a new framework for gravitational wave data analysis that extends time-delay interferometry by using principal component analysis to optimally cancel laser noise in LISA data.

Contribution

It develops PCI, a novel approach combining PCA with likelihood modeling to improve laser noise cancellation in space-based gravitational wave detection.

Findings

PCI provides an optimal data analysis framework for LISA.

The method effectively decomposes measurement covariance in frequency domain.

PCI enhances noise cancellation compared to traditional TDI methods.

Abstract

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.