Augmented kludge waveforms and Gaussian process regression for EMRI data analysis

TL;DR

This paper explores improved EMRI waveform models called augmented kludge waveforms and uses Gaussian process regression to reduce systematic errors, enhancing data analysis for future space-based gravitational-wave detectors.

Contribution

It introduces augmented kludge waveforms and applies Gaussian process regression to mitigate errors, advancing EMRI data analysis methods.

Findings

Augmented kludge waveforms improve accuracy over traditional models.

Gaussian process regression effectively marginalizes modeling errors.

Enhanced waveform models facilitate more reliable EMRI detection.

Abstract

Extreme-mass-ratio inspirals (EMRIs) will be an important type of astrophysical source for future space-based gravitational-wave detectors. There is a trade-off between accuracy and computational speed for the EMRI waveform templates required in the analysis of data from these detectors. We discuss how the systematic error incurred by using faster templates may be reduced with improved models such as augmented kludge waveforms, and marginalised over with statistical techniques such as Gaussian process regression.