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The variance and fractional variance on a fixed time window (variously known as “rms percent” or “modulation index”) are commonly used to characterize the variability of astronomical sources. We summarize properties of this statistic for a Gaussian process.

 

We develop a “dual-cone” model for millimeter wavelength emission near a spinning black hole. The model consists of optically thin, luminous cones of emission, centered on the spin axis, which are meant to represent jet walls. The resulting image is dominated by a thin ring. We first consider the effect of the black hole’s spin on the image and show that the dominant effect is to displace the ring perpendicular to the projection of the spin axis on the sky by$2a_{*}\sin i+\mathit{}(a_{*}^3)$. This effect is lower order ina_*than changes in the shape and size of the photon ring itself but is undetectable without a positional reference. We then show that the centerline of the jet can provide a suitable reference: its location is exactly independent of spin if the observer is outside the cone and nearly independent of spin if the observer is inside the cone. If astrophysical uncertainties can be controlled, then spin displacement is large enough to be detectable by future space very long baseline interferometry missions. Finally, we consider ring substructure in the dual-cone model and show that features in total intensity are not universal and depend on the cone-opening angle.

 

The Event Horizon Telescope (EHT) has produced images of M87* and Sagittarius A*, and will soon produce time sequences of images, or movies. In anticipation of this, we describe a technique to measure the rotation rate, or pattern speed Ω_p, from movies using an autocorrelation technique. We validate the technique on Gaussian random field models with a known rotation rate and apply it to a library of synthetic images of Sgr A* based on general relativistic magnetohydrodynamics simulations. We predict that EHT movies will have Ω_p≈ 1° perGMc⁻³, which is of order 15% of the Keplerian orbital frequency in the emitting region. We can plausibly attribute the slow rotation seen in our models to the pattern speed of inward-propagating spiral shocks. We also find that Ω_pdepends strongly on inclination. Application of this technique will enable us to compare future EHT movies with the clockwise rotation of Sgr A* seen in near-infrared flares by GRAVITY. Pattern speed analysis of future EHT observations of M87* and Sgr A* may also provide novel constraints on black hole inclination and spin, as well as an independent measurement of black hole mass.

 

The centers of our Galaxy and the nearby Messier 87 are known to contain supermassive black holes, which support accretion flows that radiate across the electromagnetic spectrum. Although the composition of the accreting gas is unknown, it is likely a mix of ionized hydrogen and helium. We use a simple analytic model and a suite of numerical general relativistic magnetohydrodynamic accretion simulations to study how polarimetric images and spectral energy distributions of the source are influenced by the hydrogen/helium content of the accreting matter. We aim to identify general trends rather than make quantitatively precise predictions, since it is not possible to fully explore the parameter space of accretion models. If the ion-to-electron temperature ratio is fixed, then increasing the helium fraction increases the gas temperature; to match the observational flux density constraints, the number density of electrons and magnetic field strengths must therefore decrease. In our numerical simulations, emission shifts from regions of low to high plasmaβ—both altering the morphology of the image and decreasing the variability of the light curve—especially in strongly magnetized models with emission close to the midplane. In polarized images, we find that the model gas composition influences the degree to which linear polarization is (de)scrambled and therefore affects estimates for the resolved linear polarization fraction. We also find that the spectra of helium-composition flows peak at higher frequencies and exhibit higher luminosities. We conclude that gas composition may play an important role in predictive models for black hole accretion.

 

The groundbreaking image of the black hole at the center of the M87 galaxy has raised questions at the intersection of observational astronomy and black hole physics. How well can the radius of a black hole shadow be measured, and can this measurement be used to distinguish general relativity from other theories of gravity? We explore these questions using a simple spherical flow model in general relativity, scalar Gauss–Bonnet gravity, and the Rezzolla and Zhidenko parameterized metric. We assume an optically thin plasma with power-law emissivity in radius. Along the way we present a generalized Bondi flow, as well as a piecewise analytic model for the brightness profile of a cold inflow. We use the second moment of a synthetic image as a proxy for EHT observables and compute the ratio of the second moment to the radius of the black hole shadow. We show that corrections to this ratio from modifications to general relativity are subdominant compared to corrections to the critical impact parameter, and we argue that this is generally true. In our simplified model the astrophysical parameter uncertainty dominates the gravity theory parameter uncertainty, underlining the importance of understanding the accretion model if EHT is to be used to successfully test theories of gravity.

 

We develop an approach to recover the underlying properties of fluid-dynamical processes from sparse measurements. We are motivated by the task of imaging the stochastically evolving environment surrounding black holes, and demonstrate how flow parameters can be estimated from sparse interferometric measurements used in radio astronomical imaging. To model the stochastic flow we use spatio-temporal Gaussian Random Fields (GRFs). The high dimensionality of the underlying source video makes direct representation via a GRF’s full covariance matrix intractable. In contrast, stochastic partial differential equations are able to capture correlations at multiple scales by specifying only local interaction coefficients. Our approach estimates the coefficients of a space-time diffusion equation that dictates the stationary statistics of the dynamical process. We analyze our approach on realistic simulations of black hole evolution and demonstrate its advantage over state-of-the-art dynamic black hole imaging techniques. 

The Event Horizon Telescope (EHT) recently released the first horizon-scale images of the black hole in M87. Combined with other astronomical data, these images constrain the mass and spin of the hole as well as the accretion rate and magnetic flux trapped on the hole. An important question for the EHT is how well key parameters, such as trapped magnetic flux and the associated disk models, can be extracted from present and future EHT VLBI data products. The process of modeling visibilities and analyzing them is complicated by the fact that the data are sparsely sampled in the Fourier domain while most of the theory/simulation is constructed in the image domain. Here we propose a data- driven approach to analyze complex visibilities and closure quantities for radio interferometric data with neural networks. Using mock interferometric data, we show that our neural networks are able to infer the accretion state as either high magnetic flux (MAD) or low magnetic flux (SANE), suggesting that it is possible to perform parameter extraction directly in the visibility domain without image reconstruction. We have applied VLBInet to real M87 EHT data taken on four di↵erent days in 2017 (April 5, 6, 10, 11), and our neural networks give a score prediction 0.52, 0.4, 0.43, 0.76 for each day, with an average score 0.53, which shows no significant indication for the data to lean toward either the MAD or SANE state.