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• W2893690135 abstract "We present accurate fits for the remnant properties of generically precessing binary black holes, trained on large banks of numerical-relativity simulations. We use Gaussian process regression to interpolate the remnant mass, spin, and recoil velocity in the 7-dimensional parameter space of precessing black-hole binaries with mass ratios $qleq2$, and spin magnitudes $chi_1,chi_2leq0.8$. For precessing systems, our errors in estimating the remnant mass, spin magnitude, and kick magnitude are lower than those of existing fitting formulae by at least an order of magnitude (improvement is also reported in the extrapolated region at high mass ratios and spins). In addition, we also model the remnant spin and kick directions. Being trained directly on precessing simulations, our fits are free from ambiguities regarding the initial frequency at which precessing quantities are defined. We also construct a model for remnant properties of aligned-spin systems with mass ratios $qleq8$, and spin magnitudes $chi_1,chi_2leq0.8$. As a byproduct, we also provide error estimates for all fitted quantities, which can be consistently incorporated into current and future gravitational-wave parameter-estimation analyses. Our model(s) are made publicly available through a fast and easy-to-use Python module called surfinBH." @default.
• W2893690135 created "2018-10-05" @default.
• W2893690135 creator A5011912817 @default.
• W2893690135 creator A5053449921 @default.
• W2893690135 creator A5063215482 @default.
• W2893690135 creator A5064122083 @default.
• W2893690135 creator A5087496293 @default.
• W2893690135 date "2019-01-10" @default.
• W2893690135 modified "2023-10-14" @default.
• W2893690135 title "High-Accuracy Mass, Spin, and Recoil Predictions of Generic Black-Hole Merger Remnants" @default.
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• W2893690135 doi "https://doi.org/10.1103/physrevlett.122.011101" @default.
• W2893690135 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31012642" @default.
• W2893690135 hasPublicationYear "2019" @default.
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• W2893690135 hasAuthorship W2893690135A5063215482 @default.

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