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A Parametric model for the shapes of black-hole shadows in non-Kerr spacetimes
- Publication Year :
- 2019
-
Abstract
- The Event Horizon Telescope (EHT) is taking the first images of black holes resolved at horizon scales to measure their shadows and probe accretion physics. A promising avenue for testing the hypothesis that astrophysical black holes are described by the Kerr solution to Einstein's equations is to compare the size and shape of the shadow a black hole casts on the surrounding emission to the predictions of the Kerr metric. We develop here an efficient parametric framework to perform this test. We carry out ray-tracing simulations for several parametrized non-Kerr metrics to create a large data set of non-Kerr shadows that probe the allowed parameter space for the free parameters of each metric. We then perform principal components analysis (PCA) on this set of shadows and show that only a small number of components are needed to accurately reconstruct all shadows within the set. We further show that the amplitude of the PCA components are smoothly related to the free parameters in the metrics, and therefore, that these PCA components can be fit to EHT observations in order to place constraints on the free parameters of these metrics that will help quantify any potential deviations from the Kerr solution.<br />New figure added, new simulations run correcting typo in previous work, accepted to ApJ
- Subjects :
- Physics
Event Horizon Telescope
High Energy Astrophysical Phenomena (astro-ph.HE)
010504 meteorology & atmospheric sciences
General relativity
Astrophysics::High Energy Astrophysical Phenomena
Kerr metric
FOS: Physical sciences
Astronomy and Astrophysics
General Relativity and Quantum Cosmology (gr-qc)
Parameter space
01 natural sciences
General Relativity and Quantum Cosmology
Black hole
Space and Planetary Science
0103 physical sciences
Parametric model
Metric (mathematics)
Statistical physics
Astrophysics - High Energy Astrophysical Phenomena
010303 astronomy & astrophysics
0105 earth and related environmental sciences
Parametric statistics
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....6a275094a70d772c2d3d046c874b72ae