Extending black-hole remnant surrogate models to extreme mass ratios
- Creators
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Boschini, Matteo
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Gerosa, Davide
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Varma, Vijay
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Armaza, Cristóbal
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Boyle, Michael
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Bonilla, Marceline S.
- Ceja, Andrea
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Chen, Yitian
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Deppe, Nils
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Giesler, Matthew
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Kidder, Lawrence E.
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Kumar, Prayush
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Lara, Guillermo
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Long, Oliver
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Ma, Sizheng
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Mitman, Keefe
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Nee, Peter James
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Pfeiffer, Harald P.
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Ramos-Buades, Antoni
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Scheel, Mark A.1
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Vu, Nils L.
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Yoo, Jooheon
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1.
California Institute of Technology
Abstract
Numerical-relativity surrogate models for both black-hole merger waveforms and remnants have emerged as important tools in gravitational-wave astronomy. While producing very accurate predictions, their applicability is limited to the region of the parameter space where numerical-relativity simulations are available and computationally feasible. Notably, this excludes extreme mass ratios. We present a machine-learning approach to extend the validity of existing and future numerical-relativity surrogate models toward the test-particle limit, targeting in particular the mass and spin of postmerger black-hole remnants. Our model is trained on both numerical-relativity simulations at comparable masses and analytical predictions at extreme mass ratios. We extend the gaussian-process-regression model NRSur7dq4Remnant, validate its performance via cross validation, and test its accuracy against additional numerical-relativity runs. Our fit, which we dub NRSur7dq4EmriRemnant, reaches an accuracy that is comparable to or higher than that of existing remnant models while providing robust predictions for arbitrary mass ratios.
Copyright and License
Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.
Open access publication funded by the Max Planck Society.
Acknowledgement
We thank Costantino Pacilio, Nathan Johnson-McDaniel, and Geraint Pratten for discussions. M. B. and D. G. are supported by ERC Starting Grant No. 945155–GWmining, Cariplo Foundation Grant No. 2021-0555, MUR PRIN Grant No. 2022-Z9X4XS, and the ICSC National Research Centre funded by NextGenerationEU. M. B. is supported by an Erasmus+scholarship. D. G. is supported by Leverhulme Trust Grant No. RPG-2019-350. V. V. is supported by European Union Marie Skłodowska-Curie Grant No. 896869. Computational work was performed at CINECA with allocations through INFN, Bicocca, and ISCRA project HP10BEQ9JB. This work was supported in part by the Sherman Fairchild Foundation, NSF Grants No. PHY-2207342, No. PHY-2011961, No. PHY-2011968, No. PHY-2208014, No. OAC-2209655, No. AST-2219109, the Dan Black Family Trust, Nicholas and Lee Begovich, the Indian Department of Atomic Energy under Project No. RTI4001, and by the Ashok and Gita Vaish Early Career Faculty Fellowship at the International Centre for Theoretical Sciences.
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Additional details
- ISSN
- 2470-0029
- European Research Council
- 945155
- Fondazione Cariplo
- 2021-0555
- Ministry of Education, Universities and Research
- 2022-Z9X4XS
- Leverhulme Trust
- RPG-2019-350
- Sherman Fairchild Foundation
- National Science Foundation
- PHY-2207342
- National Science Foundation
- PHY-2011961
- National Science Foundation
- PHY-2011968
- National Science Foundation
- PHY-2208014
- National Science Foundation
- OAC-2209655
- National Science Foundation
- AST-2219109
- Department of Atomic Energy
- RTI4001
- NextGenerationEU
- Erasmus + Scholarship
- Dan Black Family Trust
- Nicholas and Lee Begovich
- European Research Council
- 896869
- International Centre for Theoretical Sciences
- Max Planck Society
- Caltech groups
- TAPIR