Validation and Calibration of Semi-Analytical Models for the Event Horizon Telescope Observations of Sagittarius A*
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| Format: | Preprint |
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2025
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| _version_ | 1866910919331676160 |
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| author | SaraerToosi, Ali Broderick, Avery |
| author_facet | SaraerToosi, Ali Broderick, Avery |
| contents | 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 leverages \alinet, a generative machine learning model, to efficiently produce radiatively inefficient accretion flow (RIAF) images as a function of the specified physical parameters. \alinet has 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_18624 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Validation and Calibration of Semi-Analytical Models for the Event Horizon Telescope Observations of Sagittarius A* SaraerToosi, Ali Broderick, Avery High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics Computer Vision and Pattern Recognition Machine Learning 85A99, 35Q75, 65C60, 62F15 I.2.6; G.1.10; I.4.10 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 leverages \alinet, a generative machine learning model, to efficiently produce radiatively inefficient accretion flow (RIAF) images as a function of the specified physical parameters. \alinet has 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. |
| title | Validation and Calibration of Semi-Analytical Models for the Event Horizon Telescope Observations of Sagittarius A* |
| topic | High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics Computer Vision and Pattern Recognition Machine Learning 85A99, 35Q75, 65C60, 62F15 I.2.6; G.1.10; I.4.10 |
| url | https://arxiv.org/abs/2504.18624 |