Cauchy-characteristic extraction in numerical relativity
N. Bishop, R. Gomez, L. Lehner, J. Winicour
Abstract
We treat the calculation of gravitational radiation using the mixed timelike-null initial value formulation of general relativity. The determination of an exterior radiative solution is based on boundary values on a timelike worldtube Γ and on characteristic data on an outgoing null cone emanating from an initial cross-section of Γ. We present the details of a 3-dimensional computational algorithm which evolves this initial data on a numerical grid, which is compactified to include future null infinity as finite grid points. A code implementing this algorithm is calibrated in the quasispherical regime. We consider the application of this procedure to the extraction of waveforms at infinity from an interior Cauchy evolution, which provides the boundary data on Γ. This is a first step towards Cauchy-characteristic matching in which the data flow at the boundary Γ is two-way, with the Cauchy and characteristic computations providing exact boundary values for each other. We describe strategies for implementing matching and show that for small target error it is much more computationally efficient than alternative methods.
Create a lesson
Related papers
All Quiet on the Vector Front: Scalar Growth and Perturbative Control in Horndeski--Weyl-squared Gravity
Antonio De Felice, Guillem Domènech, Jan Tränkle
Directional effects of the Unruh effect on steering harvesting
Shu-Min Wu, Rui-Yang Xu, Zhihong Liu
Testing Charged Black Holes Embedded in Anisotropic Matter and Surrounded by Plasma through Gravitational Lensing: Bounds from the Sloan Lens ACS Survey
Ritesh Pandey, Shubham Kala, Amare Abebe et al.
Quantum noise and torque correlations in a torsion-bar Sagnac speed meter
Yuki Inoue, Mario Onglao, Daiki Tanabe et al.
A catalog of long, eccentric and precessing binary black hole simulations from ICTS-TIFR
Vaishak Prasad, Prayush Kumar, Michael Boyle et al.
Hierarchical Parameter Estimation for Intrinsic Parameters of Extreme-mass-ratio Inspirals Using Deep Learning
Ping Shen, Wen-Biao Han, Minghui Du