2 Fast 2 Fiducial: Gaussian processes for the interpolation and marginalization of waveform error in extreme-mass-ratio-inspiral parameter estimation

TL;DR

This paper introduces a Gaussian process-based method to interpolate and marginalize waveform errors, addressing systematic biases in parameter estimation for extreme-mass-ratio inspirals in gravitational-wave data analysis.

Contribution

The paper adapts Gaussian processes to interpolate waveform errors and marginalize over them, providing a novel approach to reduce systematic bias in gravitational-wave parameter estimation.

Findings

Gaussian process interpolation effectively models waveform errors

Marginalization reduces systematic bias in parameter estimates

Method demonstrated successfully on LISA Data Challenge data

Abstract

A number of open problems hinder our present ability to extract scientific information from data that will be gathered by the near-future gravitational-wave mission LISA. Many of these relate to the modeling, detection and characterization of signals from binary inspirals with an extreme component-mass ratio of $\lesssim10^{-4}$. In this paper, we draw attention to the issue of systematic error in parameter estimation due to the use of fast but approximate waveform models; this is found to be relevant for extreme-mass-ratio inspirals even in the case of waveforms with $\gtrsim90\%$ overlap accuracy and moderate ($\gtrsim30$) signal-to-noise ratios. A scheme that uses Gaussian processes to interpolate and marginalize over waveform error is adapted and investigated as a possible precursor solution to this problem. Several new methodological results are obtained, and the viability of the technique is successfully demonstrated on a three-parameter example in the setting of the LISA Data Challenge.