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

physical review. d/physical review. d.

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