Using mismatch distances computed at best-fit parameters, per parameter, the paper predicts accurate bias SNRs for aligned-spin binary black hole measurements and derives waveform accuracy requirements for next-generation detectors.
2 Fast 2 Fiducial: Gaussian processes for the interpolation and marginalization of waveform error in extreme-mass-ratio-inspiral parameter estimation
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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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On the use and interpretation of signal-model indistinguishability measures for gravitational-wave astronomy
Using mismatch distances computed at best-fit parameters, per parameter, the paper predicts accurate bias SNRs for aligned-spin binary black hole measurements and derives waveform accuracy requirements for next-generation detectors.