A Gaussian Process Regression model trained on an archive of eccentricity-reduced binary black hole simulations predicts initial conditions that achieve low eccentricity with zero or one iteration.
Conformal ``thin sandwich'' data for the initial-value problem of general relativity
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abstract
The initial-value problem is posed by giving a conformal three-metric on each of two nearby spacelike hypersurfaces, their proper-time separation up to a multiplier to be determined, and the mean (extrinsic) curvature of one slice. The resulting equations have the {\it same} elliptic form as does the one-hypersurface formulation. The metrical roots of this form are revealed by a conformal ``thin sandwich'' viewpoint coupled with the transformation properties of the lapse function.
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Eccentric BBH signals recovered with quasi-circular precessing models show biases in chirp mass and χ_p; Bayes factors favor eccentric aligned-spin models when both eccentricity and precession are present.
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Data-Driven Acceleration of Eccentricity Reduction for Binary Black Hole Simulations
A Gaussian Process Regression model trained on an archive of eccentricity-reduced binary black hole simulations predicts initial conditions that achieve low eccentricity with zero or one iteration.
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Biased parameter inference of eccentric, spin-precessing binary black holes
Eccentric BBH signals recovered with quasi-circular precessing models show biases in chirp mass and χ_p; Bayes factors favor eccentric aligned-spin models when both eccentricity and precession are present.