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High-Accuracy Mass, Spin, and Recoil Predictions of Generic Black-Hole Merger Remnants

Varma, Vijay and Gerosa, Davide and Stein, Leo C. and Hébert, François and Zhang, Hao (2019) High-Accuracy Mass, Spin, and Recoil Predictions of Generic Black-Hole Merger Remnants. Physical Review Letters, 122 (1). Art. No. 011101. ISSN 0031-9007. https://resolver.caltech.edu/CaltechAUTHORS:20190110-130026348

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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 seven-dimensional parameter space of precessing black-hole binaries with mass ratios q ≤ 2, and spin magnitudes χ_1, χ_2 ≤ 0.8. 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 q ≤ 8, and spin magnitudes χ_1, χ_2 ≤ 0.8. 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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1103/physrevlett.122.011101DOIArticle
https://arxiv.org/abs/1809.09125arXivDiscussion Paper
Additional Information:© 2019 American Physical Society. Received 26 September 2018; revised manuscript received 5 November 2018; published 10 January 2019. We thank Jonathan Blackman, Stephen Taylor, David Keitel, Anuradha Gupta, and Serguei Ossokine for useful discussions. We made use of the public LIGO Algorithm Library [87] in the evaluation of existing fitting formulae and to perform PN evolutions. We thank Nathan Johnson-McDaniel for useful discussions, comments on the manuscript, and for sharing his code to evaluate the HLZ kick fits. V. V. and F. H. are supported by the Sherman Fairchild Foundation and NSF Grants No. PHY–1404569, No. PHY–170212, and No. PHY–1708213 at Caltech. D. G. is supported by NASA through Einstein Postdoctoral Fellowship Grant No. PF6–170152 awarded by the Chandra X-ray Center, which is operated by the Smithsonian Astrophysical Observatory for NASA under Contract No. NAS8-03060. L. C. S. acknowledges support from NSF Grant No. PHY–1404569 and the Brinson Foundation. H. Z. acknowledges support from the Caltech SURF Program and NSF Grant No. PHY–1404569. Computations were performed on NSF/NCSA Blue Waters under allocation NSF PRAC–1713694 and on the Wheeler cluster at Caltech, which is supported by the Sherman Fairchild Foundation and by Caltech.
Group:TAPIR
Funders:
Funding AgencyGrant Number
Sherman Fairchild FoundationUNSPECIFIED
NSFPHY-1404569
NSFPHY-170212
NSFPHY-1708213
NASA Einstein FellowshipPF6-170152
NASANAS8-03060
Brinson FoundationUNSPECIFIED
Caltech Summer Undergraduate Research Fellowship (SURF)UNSPECIFIED
NSFPRAC-1713694
Issue or Number:1
Record Number:CaltechAUTHORS:20190110-130026348
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190110-130026348
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:92199
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:11 Jan 2019 05:09
Last Modified:03 Oct 2019 20:42

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