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

Ivo SengoDepartamento de Matemática da Universidade de Aveiro, Centre for Research and Development in Mathematics and Applications (CIDMA), Campus de Santiago, 3810-183 Aveiro, PortugalPedro V. P. CunhaDepartamento de Matemática da Universidade de Aveiro, Centre for Research and Development in Mathematics and Applications (CIDMA), Campus de Santiago, 3810-183 Aveiro, PortugalCarlos HerdeiroDepartamento de Matemática da Universidade de Aveiro, Centre for Research and Development in Mathematics and Applications (CIDMA), Campus de Santiago, 3810-183 Aveiro, PortugalEugen RaduDepartamento de Matemática da Universidade de Aveiro, Centre for Research and Development in Mathematics and Applications (CIDMA), Campus de Santiago, 3810-183 Aveiro, Portugal
2023en
ABI

Abstract

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.

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