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Title: Validation and Calibration of Semianalytical Models for the Event Horizon Telescope Observations of Sagittarius A*
Abstract The Event Horizon Telescope (EHT) enables the exploration of black hole accretion flows at event-horizon scales. Fitting ray-traced physical models to EHT observations requires the generation of synthetic images, a task that is computationally demanding. This study leveragesALINet, a generative machine learning model, to efficiently produce radiatively inefficient accretion flow (RIAF) images as a function of the specified physical parameters.ALINethas previously been shown to be able to interpolate black hole images and their associated physical parameters after training on a computationally tractable set of library images. We utilize this model to estimate the uncertainty introduced by a number of anticipated unmodeled physical effects, including interstellar scattering and intrinsic source variability. We then use this to calibrate physical parameter estimates and their associated uncertainties from RIAF model fits to mock EHT data via a library of general relativistic magnetohydrodynamics models.  more » « less
Award ID(s):
2034306
PAR ID:
10660247
Author(s) / Creator(s):
;
Publisher / Repository:
IOP
Date Published:
Journal Name:
The Astrophysical Journal
Volume:
991
Issue:
1
ISSN:
0004-637X
Page Range / eLocation ID:
105
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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