EHT observables as a tool to estimate parameters of supermassive black holes

TL;DR

This paper introduces a new method to estimate supermassive black hole parameters using EHT observables, explicitly accounting for measurement uncertainties, and applies it to model Kerr and related spacetimes for M87* and Sgr A*.

Contribution

The paper presents a novel technique for estimating SMBH parameters from EHT data that considers measurement uncertainties and applies to more general shadow shapes.

Findings

Method provides consistent estimates with existing formalisms.

Can estimate charge, spin, and inclination angles of SMBHs.

Applicable to future more precise EHT measurements.

Abstract

The Event Horizon Telescope (EHT) collaboration unveiled event-horizon-scale images of the supermassive black holes (SMBHs) M87* and Sgr A*, revealing a dark brightness depression, namely the black hole shadow, whose shape and size may encode the parameters of the SMBHs, and the shadow is consistent with that of a Kerr black hole. It furnishes another encouraging tool to estimate black hole parameters and test theories of gravity in extreme regions near the event horizon. We propose a technique that uses EHT observables, the angular shadow diameter $d_{sh}$ and the axis ratio $\mathcal{D}_A$, to estimate the parameters associated with SMBHs, described by the Kerr metric. Unlike previous methods, our approach explicitly considers the uncertainties in the measurement of EHT observables. Modelling Kerr--Newman and three rotating regular spacetimes to be M87* and Sgr A* and applying our technique, we estimate the associated charge parameters along with spin. Our method is consistent with the existing formalisms and can be applied to shadow shapes that are more general and may not be circular. We can use the technique for other SMBHs once their EHT observables become accessible. With future, more accurate measurements of the EHT observables, the estimation of various SMBH parameters like the spin and inclination angles of M87* and Sgr A* would be more precise.