An Upper Limit on the Charge of the Black Hole Sgr A* from EHT Observations

TL;DR

This study uses EHT observations of Sgr A* to set upper limits on its charge within Kerr-like black hole models, finding more stringent constraints than previous M87* results and highlighting the potential of future observations.

Contribution

It provides the first constraints on the charge of Sgr A* using Kerr-like black hole models based on EHT data, comparing different models and analyzing future observational prospects.

Findings

EHT data constrains the charge of Sgr A* more tightly than M87*.

Kerr--Newman and Horndeski black holes are limited by current observations.

Future EHT experiments could further refine charge constraints.

Abstract

The Event Horizon Telescope (EHT) recently released an image of the supermassive black hole Sgr A* showing an angular shadow diameter $d_{sh}= 48.7 \pm 7\,\mu$as and Schwarzschild shadow deviation $\delta = -0.08^{+0.09}_{-0.09}~\text{(VLTI)},-0.04^{+0.09}_{-0.10}~\text{(Keck)}$ using a black hole mass $M = 4.0^{+1.1}_{-0.6} \times 10^6 M_\odot $. The EHT image of Sgr A* is consistent with a Kerr black hole's expected appearance, and the results directly prove the existence of a supermassive black hole at the center of the Milky Way. Here, we use the EHT observational results for Sgr A* to investigate the constraints on its charge with the aid of Kerr-like black holes, paying attention to three leading rotating models, namely Kerr--Newman, Horndeski, and hairy black holes. Modeling the supermassive black hole Sgr A* as these Kerr-like black holes, we observe that the EHT results of Sgr A* place more strict upper limits on the parameter space of Kerr--Newman and Horndeski black holes than those placed by the EHT results for M87*. A systematic bias analysis reveals that, observational results of future EHT experiments place more precise limits on the charge of black hole Sgr A*. Thus, the Kerr-like black holes and Kerr black holes are indiscernible in a substantial region of the EHT-constrained parameter space; the claim is substantiated by our bias analysis.