Use of Singular-Value Decomposition in Gravitational-Wave Data Analysis

TL;DR

This paper reviews how singular-value decomposition (SVD) enhances gravitational-wave data analysis by reducing computational costs, improving parameter estimation, and enabling waveform interpolation.

Contribution

It summarizes the application of SVD techniques in gravitational-wave data analysis, highlighting its benefits in efficiency and accuracy.

Findings

SVD produces basis waveforms for matched filtering.

SVD decreases computational costs in waveform searches.

SVD improves parameter estimation and waveform interpolation.

Abstract

Singular-value decomposition is a powerful technique that has been used in the analysis of matrices in many fields. In this paper, we summarize how it has been applied to the analysis of gravitational-wave data. These include producing basis waveforms for matched filtering, decreasing the computational cost of searching for many waveforms, improving parameter estimation, and providing a method of waveform interpolation.