Non-ideal magnetohydrodynamic (MHD) effects may play a significant role in determining the dynamics, thermal properties, and observational signatures of radiatively inefficient accretion flows onto black holes. In particular, particle acceleration during magnetic reconnection events may influence black hole spectra and flaring properties. We use representative general relativistic magnetohydrodynamic (GRMHD) simulations of black hole accretion flows to identify and explore the structures and properties of current sheets as potential sites of magnetic reconnection. In the case of standard and normal evolution (SANE) disks, we find that in the reconnection sites, the plasma beta ranges from 0.1 to 1000, the magnetization ranges from 10(-4) to 1, and the guide fields are weak compared with the reconnecting fields. In magnetically arrested (MAD) disks, we find typical values for plasma beta from 10(-2) to 10(3), magnetizations from 10(-3) to 10, and typically stronger guide fields, with strengths comparable to or greater than the reconnecting fields. These are critical parameters that govern the electron energy distribution resulting from magnetic reconnection and can be used in the context of plasma simulations to provide microphysics inputs to global simulations. We also find that ample magnetic energy is available in the reconnection regions to power the fluence of bright X-ray flares observed from the black hole in the center of the Milky Way.

Closure phases along different baseline triangles carry a large amount of information regarding the structures of the images of black holes in interferometric observations with the Event Horizon Telescope. We use long time span, high cadence, GRMHD+radiative transfer models of Sgr A* to investigate the expected variability of closure phases in such observations. We find that, in general, closure phases along small baseline triangles show little variability, except in the cases when one of the triangle vertices crosses one of the small regions of low visibility amplitude. The closure phase variability increases with the size of the baseline triangle, as larger baselines probe the small-scale structures of the images, which are highly variable. On average, the funnel-dominated MAD models show less closure phase variability than the disk-dominated SANE models, even in the large baseline triangles, because the images from the latter are more sensitive to the turbulence in the accretion flow. Our results suggest that image reconstruction techniques need to explicitly take into account the closure phase variability, especially if the quality and quantity of data allow for a detailed characterization of the nature of variability. This also implies that, if image reconstruction techniques that rely on the assumption of a static image are utilized, regions of the u-v space that show a high level of variability will need to be identified and excised.

The increasing number and precision of measurements of neutron star masses, radii, and, in the near future, moments of inertia offer the possibility of precisely determining the neutron star equation of state (EOS). One way to facilitate the mapping of observables to the EOS is through a parametrization of the latter. We present here a generic method for optimizing the parametrization of any physically allowed EOS. We use mock EOS that incorporate physically diverse and extreme behavior to test how well our parametrization reproduces the global properties of the stars, by minimizing the errors in the observables of mass, radius, and the moment of inertia. We find that using piecewise polytropes and sampling the EOS with five fiducial densities between similar to 1-8 times the nuclear saturation density results in optimal errors for the smallest number of parameters. Specifically, it recreates the radii of the assumed EOS to within less than 0.5 km for the extreme mock EOS and to within less than 0.12 km for 95% of a sample of 42 proposed, physically motivated EOS. Such a parametrization is also able to reproduce the maximum mass to within 0.04 M-circle dot and the moment of inertia of a 1.338 M-circle dot. neutron star to within less than 10% for 95% of the proposed sample of EOS.

Significant X-ray variability and flaring has been observed from Sgr A* but is poorly understood from a theoretical standpoint. We perform general relativistic magnetohydrodynamic simulations that take into account a population of non-thermal electrons with energy distributions and injection rates that are motivated by PIC simulations of magnetic reconnection. We explore the effects of including these non-thermal electrons on the predicted broadband variability of Sgr A* and find that X-ray variability is a generic result of localizing non-thermal electrons to highly magnetized regions, where particles are likely to be accelerated via magnetic reconnection. The proximity of these high-field regions to the event horizon forms a natural connection between IR and X-ray variability and accounts for the rapid timescales associated with the X-ray flares. The qualitative nature of this variability is consistent with observations, producing X-ray flares that are always coincident with IR flares, but not vice versa, i.e., there are a number of IR flares without X-ray counterparts.

We explore the use of principal component analysis (PCA) to characterize high-fidelity simulations and interferometric observations of the millimeter emission that originates near the horizons of accreting black holes. We show mathematically that the Fourier transforms of eigenimages derived from PCA applied to an ensemble of images in the spatial domain are identical to the eigenvectors of PCA applied to the ensemble of the Fourier transforms of the images, which suggests that this approach may be applied to modeling the sparse interferometric Fourier-visibilities produced by an array such as the Event Horizon Telescope. We also show that the simulations in the spatial domain can themselves be compactly represented with a PCA-derived basis of eigenimages, which allows for detailed comparisons to be made between variable observations and time-dependent models, as well as for detection of outliers or rare events within a time series of images. Furthermore, we demonstrate that the spectrum of PCA eigenvalues is a diagnostic of the power spectrum of the structure and, hence, of the underlying physical processes in the simulated and observed images.