Parallel adaptive event horizon finder for numerical relativity

Creators: Bohn, Andy; Kidder, Lawrence E.; Teukolsky, Saul A.

Abstract

With Advanced LIGO detecting the gravitational waves emitted from a pair of merging black holes in late 2015, we have a new perspective into the strong field regime of binary black hole systems. Event horizons are the defining features of such black hole spacetimes. We introduce a new code for locating event horizons in numerical simulations based on a Delaunay triangulation on a topological sphere. The code can automatically refine arbitrary regions of the event horizon surface to find and explore features such as the hole in a toroidal event horizon, as discussed in our companion paper. We also investigate various ways of integrating the geodesic equation and find evolution equations that can be integrated efficiently with high accuracy.

Additional Information

© 2016 American Physical Society. (Received 7 June 2016; published 2 September 2016) We thank David Nichols for useful conversations about affine parametrizations of event horizon generators. We are grateful to François Hébert and William Throwe for various helpful conversations including numerous triangle drawing whiteboard sessions. We thank Daniel A. Hemberger for helping us understand the intricacies of the adaptive mesh refinement in spec. For helping smooth the visualization of event horizon surfaces, we thank Curran D. Muhlberger. We also thank Harald Pfeiffer for providing the BBH simulation with parameters similar to the system detected by Advanced LIGO, shown in Fig. 11. For providing useful suggestions during the writing phase, we thank Nils Deppe. We gratefully acknowledge support for this research at Cornell from the Sherman Fairchild Foundation and NSF Grants No. PHY-1306125 and No. AST-1333129. Calculations were performed on the Zwicky cluster at Caltech, which is supported by the Sherman Fairchild Foundation and by NSF Grant No. PHY-0960291; on the NFS XSEDE network under Grant No. TG-PHY990007N; at the GPC supercomputer at the SciNet HPC Consortium [40] [SciNet is funded by the Canada Foundation for Innovation (CFI) under the auspices of Compute Canada]; the Government of Ontario; Ontario Research Fund (ORF)—Research Excellence; and the University of Toronto. All the surface visualizations were done using Paraview [41].

Attached Files

Published - PhysRevD.94.064008.pdf

Submitted - 1606.00437

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PhysRevD.94.064008.pdf

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