First Sagittarius A* Event Horizon Telescope Results. V. Testing Astrophysical Models of the Galactic Center Black Hole

In this paper we provide a first physical interpretation for the Event Horizon Telescope's (EHT) 2017 observations of Sgr A*. Our main approach is to compare resolved EHT data at 230 GHz and unresolved non-EHT observations from radio to X-ray wavelengths to predictions from a library of models based on time-dependent general relativistic magnetohydrodynamics simulations, including aligned, tilted, and stellar-wind-fed simulations; radiative transfer is performed assuming both thermal and nonthermal electron distribution functions. We test the models against 11 constraints drawn from EHT 230 GHz data and observations at 86 GHz, 2.2 mu m, and in the X-ray. All models fail at least one constraint. Light-curve variability provides a particularly severe constraint, failing nearly all strongly magnetized (magnetically arrested disk (MAD)) models and a large fraction of weakly magnetized models. A number of models fail only the variability constraints. We identify a promising cluster of these models, which are MAD and have inclination i <= 30 degrees. They have accretion rate (5.2-9.5) x 10(-9) M (circle dot) yr(-1), bolometric luminosity (6.8-9.2) x 10(35) erg s(-1), and outflow power (1.3-4.8) x 10(38) erg s(-1). We also find that all models with i >= 70 degrees fail at least two constraints, as do all models with equal ion and electron temperature; exploratory, nonthermal model sets tend to have higher 2.2 mu m flux density; and the population of cold electrons is limited by X-ray constraints due to the risk of bremsstrahlung overproduction. Finally, we discuss physical and numerical limitations of the models, highlighting the possible importance of kinetic effects and duration of the simulations.

Automated summary

This paper provides a first physical interpretation for the Event Horizon Telescope's observations of Sgr A*. By comparing different observations and models, the study identifies promising models while also highlighting their limitations and discussing possible improvements.