Kerr black holes with synchronised Proca hair: lensing, shadows and EHT constraints

TL;DR

This paper explores Kerr black holes with synchronized Proca hair, analyzing their lensing and shadows, and assesses how current and future observations from the EHT can constrain these exotic solutions, which interpolate between Kerr-like and non-Kerr-like geometries.

Contribution

It introduces a family of Kerr black holes with Proca hair, characterizes their optical features, and evaluates their compatibility with current EHT observational constraints.

Findings

Kerr black holes with Proca hair can produce diverse shadow shapes, including cuspy and ghost shadows.

Current EHT data allows for black holes with up to 40% Proca hair energy, within observational error margins.

Future observations require higher resolution to distinguish these exotic black hole solutions.

Abstract

We investigate the gravitational lensing by spinning Proca stars and the shadows and lensing by Kerr black holes (BHs) with synchronised Proca hair, discussing both theoretical aspects and observational constraints from the Event Horizon Telescope (EHT) M87* and Sgr A* data. On the theoretical side, this family of BHs interpolates between Kerr-like solutions -- exhibiting a similar optical appearance to that of Kerr BHs -- to very non-Kerr like solutions, exhibiting exotic features such as cuspy shadows, egg-like shadows and ghost shadows. We interpret these features in terms of the structure of the fundamental photon orbits, for which different branches exist, containing both stable and unstable orbits, with some of the latter not being shadow related. On the observational side, we show that current EHT constraints are compatible with all such BHs that could form from the growth of the superradiant instability of Kerr BHs. Unexpectedly, given the (roughly) 10% error bars in the EHT data -- and in contrast to their scalar cousin model --, some of the BHs with up to 40% of their energy in their Proca hair are compatible with the current data. We estimate the necessary resolution of future observations to better constrain this model.