Work

Bayesian Inference of Primary Hair from Black Hole Shadows in Generalized Proca Theory

Year: 2026
Type: preprint
Abstract: We investigate black hole shadows and observational constraints on primary vector hair in an exact rotating solution of generalized Proca theory. Selecting a coupling slice yields a simple, one-parameter Kerr deformation that is noncircular and depends on spin and latitude. The deformation vanishes in the nonrotating limit, while the vector field remains nontrivial. We determine the domain admitting a regular outer horizon and construct shadow and lensing images by numerical ray tracing. Ray-traced images show a modest enlargement of the shadow and changes in the surrounding lensing pattern. Bayesian inference using the shadow area-equivalent radius and axis ratio yields joint constraints on hair, spin, and viewing inclination from Event Horizon Telescope observations of M87* and Sgr A*. The M87* posterior favors relatively low spin and exhibits a pronounced spin--hair anticorrelation, while Sgr A* yields upper limits on spin and hair with its inclination remaining essentially unconstrained. These results connect the optical signatures of primary vector hair to quantitative horizon-scale constraints.
Language: English
Open Access status: green
OpenAlex ID: W7223045396
Cites: 110
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Related to: 10