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arXiv · 1912.11543

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

Alvin J. K. Chua·Natalia Korsakova·Christopher J. Moore·Jonathan R. Gair·Stanislav Babak
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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.

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Alvin J. K. Chua, Natalia Korsakova, Christopher J. Moore, Jonathan R. Gair, Stanislav Babak. 2019-12-24. 2 Fast 2 Fiducial: Gaussian processes for the interpolation and marginalization of waveform error in extreme-mass-ratio-inspiral parameter estimation. https://doi.org/10.1103/physrevd.101.044027

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