Model-independent time-delay interferometry based on principal component analysis

TL;DR

This paper introduces a novel, data-driven PCA-based method for time-delay interferometry that effectively cancels laser frequency noise in gravitational-wave detection without prior modeling, matching the sensitivity of traditional techniques.

Contribution

It presents a model-independent PCA approach to TDI, eliminating the need for prior knowledge of noise relationships or time-delays, simplifying gravitational-wave data analysis.

Findings

PCA-based TDI achieves sensitivity comparable to classic methods.

The method cancels laser frequency noise without prior modeling.

It offers a fully data-driven alternative to traditional TDI techniques.

Abstract

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.