Two new surrogate models, trained on NR simulations, predict remnant properties and eccentricity dynamics for nonspinning eccentric black hole binaries with q ≤ 4 and e < 0.23.
Characterizing the effect of eccentric- ity on the dynamics of binary black hole mergers in nu- merical relativity
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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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Merger remnant and eccentricity dynamics surrogates for eccentric nonspinning black hole binaries
Two new surrogate models, trained on NR simulations, predict remnant properties and eccentricity dynamics for nonspinning eccentric black hole binaries with q ≤ 4 and e < 0.23.
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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.