General Relativistic Magnetohydrodynamic and Monte Carlo Modeling of Sagittarius A*

We present results of models of the physical space and parameters of the accretion disk of Sagittarius A*, as well as simulations of its emergent spectrum. This begins with HARM, a 2D general relativistic magneto-hydrodynamic (GRMHD) model, specifically set up to evolve the space around a black hole. Data from HARM are then fed into a 2D Monte-Carlo (MC) code which generates and tracks emitted photons, allowing for absorption and scattering before they escape the volume.Comment: Accepted for publication in Astrophysics & Space Science, originally presented at HEDLA 201

Studies of Low Luminosity Active Galactic Nuclei with Monte Carlo and Magnetohydrodynamic Simulations

Results from several studies are presented which detail explorations of the physical and spectral properties of low luminosity active galactic nuclei. An initial Sagittarius A* general relativistic magnetohydrodynamic simulation and Monte Carlo radiation transport model suggests accretion rate changes as the dominant flaring method. A similar study on M87 introduces new methods to the Monte Carlo model for increased consistency in highly energetic sources. Again, accretion rate variation seems most appropriate to explain spectral transients. To more closely resolve the methods of particle energization in active galactic nuclei accretion disks, a series of localized shearing box simulations explores the effect of numerical resolution on the development of current sheets. A particular focus on numerically describing converged current sheet formation will provide new methods for consideration of turbulence in accretion disks

Uncertainty in orthorhombic model building: Analysis, mitigation, and validation

As seismic processing technologies have advanced, model definitions have grown correspondingly more complex, progressing from isotropy, to transverse isotropy, to the much more general orthorhombic anisotropy. This growth, while supported partly by continued improvements in acquisition techniques and survey geometries, has progressed at such a rate that current projects require solutions to a much greater relative number of unknowns than those in the near past. This added complexity creates ill-posed problems with huge model spaces, leading to a high degree of uncertainty in the final solutions. This uncertainty can be greatly mitigated through pragmatic use of a priori knowledge and intelligent data leverage and model regularization. Orthorhombic anisotropy has been well characterized both microscopically and macroscopically. This understanding allows an unprecedented level of constraint and confirmation for the model-building process by validating the directionality and strength of azimuthal velocity variations. Information within the data itself can also lead to a much more well-determined model-building result. The observed structure can be used to precondition inversion results to ensure geologic plausibility, constraining similar updates to similar events. Concurrently, nonparameterized residual moveout picking allows for complete freedom in describing gather events, yielding results which best resolve the various anisotropic parameters by accurately fitting the gather data and generating a high-resolution model. By properly combining the various constraints available in a well-designed processing project, the myriad uncertainties that arise when moving to an orthorhombic model space can be simplified and reduced, allowing for geologically reasonable solutions that fit data, yield valuable structural information, and enhance interpretation possibilities. </jats:p