Parameter control for binary black hole initial data
Abstract
When numerically solving Einstein’s equations for binary black holes (BBH), we must find initial data on a three-dimensional spatial slice by solving constraint equations. The construction of initial data is a multistep process, in which one first chooses freely specifiable data that define a conformal background and impose boundary conditions. Then, one numerically solves elliptic equations and calculates physical properties such as horizon masses, spins, and asymptotic quantities from the solution. To achieve desired properties, one adjusts the free data in an iterative “control” loop. Previous methods for these iterative adjustments rely on Newtonian approximations and do not allow the direct control of total energy and angular momentum of the system, which becomes particularly important in the study of hyperbolic encounters of black holes. Using the spectre code, we present a novel parameter control procedure that benefits from Broyden’s method in all controlled quantities. We use this control scheme to minimize drifts in bound orbits and to enable the construction of hyperbolic encounters. We see that the activation of off-diagonal terms in the control Jacobian gives us better efficiency when compared to the simpler implementation in the Spectral Einstein Code. We demonstrate robustness of the method across extreme configurations, including spin magnitudes up to 𝜒=0.9999, mass ratios up to 𝑞 =50, and initial separations up to 𝐷0 =1000𝑀. Given the open-source nature of spectre, this is the first time a parameter control scheme for constructing bound and unbound BBH initial data is available to the numerical-relativity community.
Copyright and License
© 2025 American Physical Society.
Acknowledgement
I. B. M. thanks Saul Teukolsky and Mark Scheel for helpful discussions, as well as the rest of the spectre development team for the collaborative work. Computations were performed with the spec and spectre codes [48,52] on the CaltechHPC cluster at Caltech and Urania at the Max Planck Institute Computing and Data Facility. The figures in this article were produced with matplotlib [70,71] and tikz [72]. This work was supported in part by the Sherman Fairchild Foundation and the National Science Foundation under Grants No. PHY-2309211, No. PHY-2309231, and No. OAC-2209656 at Caltech.
Supplemental Material
Code input files to reproduce published results.
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Additional details
Related works
- Is new version of
- Discussion Paper: arXiv:2509.07291 (arXiv)
Funding
- Sherman Fairchild Foundation
- National Science Foundation
- PHY-2309211
- National Science Foundation
- PHY-2309231
- National Science Foundation
- OAC-2209656
- California Institute of Technology
Dates
- Submitted
-
2025-09-08
- Accepted
-
2025-11-20