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Published March 7, 2019 | Accepted Version
Journal Article Open

Numerical black hole initial data and shadows in dynamical Chern–Simons gravity

Abstract

We present a scheme for generating first-order metric perturbation initial data for an arbitrary background and source. We then apply this scheme to derive metric perturbations in order-reduced dynamical Chern–Simons gravity (dCS). In particular, we solve for metric perturbations on a black hole background that are sourced by a first-order dCS scalar field. This gives us the leading-order metric perturbation to the spacetime in dCS gravity. We then use these solutions to compute black hole shadows in the linearly perturbed spacetime by evolving null geodesics. We present a novel scheme to decompose the shape of the shadow into multipoles parametrized by the spin of the background black hole and the perturbation parameter . We find that we can differentiate the presence of a pure Kerr spacetime from a spacetime with a dCS perturbation using the shadow, allowing in part for a null-hypothesis test of general relativity. We then consider these results in the context of the event horizon telescope.

Additional Information

© 2019 IOP Publishing Ltd. Received 12 October 2018, revised 15 December 2018; Accepted for publication 9 January 2019; Published 4 February 2019. We would like to thank Leo Stein for many useful discussions and providing us with the code used to generate the initial data in [32]. We would also like to thank Francois Hebert for helping us use the SXS lensing code. We thank Harald Pfeiffer and Leo Stein for careful reading of this manuscript. This work was supported in part by the Sherman Fairchild Foundation, and NSF grants PHY-1708212 and PHY-1708213 at Caltech and PHY-1606654 at Cornell. Computations were performed using the Spectral Einstein Code [30], including the Simulating Extreme Spacetimes collaboration lensing code [40]. All computations were performed on the Wheeler cluster at Caltech, which is supported by the Sherman Fairchild Foundation and by Caltech.

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August 19, 2023
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