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.
Excision boundary conditions for black hole initial data
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abstract
We define and extensively test a set of boundary conditions that can be applied at black hole excision surfaces when the Hamiltonian and momentum constraints of general relativity are solved within the conformal thin-sandwich formalism. These boundary conditions have been designed to result in black holes that are in quasiequilibrium and are completely general in the sense that they can be applied with any conformal three-geometry and slicing condition. Furthermore, we show that they retain precisely the freedom to specify an arbitrary spin on each black hole. Interestingly, we have been unable to find a boundary condition on the lapse that can be derived from a quasiequilibrium condition. Rather, we find evidence that the lapse boundary condition is part of the initial temporal gauge choice. To test these boundary conditions, we have extensively explored the case of a single black hole and the case of a binary system of equal-mass black holes, including the computation of quasi-circular orbits and the determination of the inner-most stable circular orbit. Our tests show that the boundary conditions work well.
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UNVERDICTED 2representative citing papers
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.