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Abstract A new image-reconstruction algorithm, Principal-component Interferometric Modeling (PRIMO), applied to the interferometric data of the M87 black hole collected with the Event Horizon Telescope (EHT), resulted in an image that reached the native resolution of the telescope array.PRIMOis based on learning a compact set of image building blocks obtained from a large library of high-fidelity, physics-based simulations of black hole images. It uses these building blocks to fill the sparse Fourier coverage of the data that results from the small number of telescopes in the array. In this paper, we show that this approach is readily justified. Since the angular extent of the image of the black hole and of its inner accretion flow is finite, the Fourier space domain is heavily smoothed, with a correlation scale that is at most comparable to the sizes of the data gaps in the coverage of Fourier space with the EHT. Consequently,PRIMOor other machine learning algorithms can faithfully reconstruct the images without the need to generate information that is unconstrained by the data within the resolution of the array. We also address the completeness of the eigenimages and the compactness of the resulting representation. We show thatPRIMOprovides a compact set of eigenimages that have sufficient complexity to recreate a broad set of images well beyond those in the training set.
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This content will become publicly available on May 13, 2026
Mahakala : A Python -based Modular Ray-tracing and Radiative Transfer Algorithm for Curved Spacetimes
Abstract We introduceMahakala, aPython-based, modular, radiative ray-tracing code for curved spacetimes. We employ Google’sJAXframework for accelerated automatic differentiation, which can efficiently compute Christoffel symbols directly from the metric, allowing the user to easily and quickly simulate photon trajectories through non-Kerr spacetimes.JAXalso enablesMahakalato run in parallel on both CPUs and GPUs.Mahakalanatively uses the Cartesian Kerr–Schild coordinate system, which avoids numerical issues caused by the pole in spherical coordinate systems. We demonstrateMahakala’s capabilities by simulating 1.3 mm wavelength images (the wavelength of Event Horizon Telescope observations) of general relativistic magnetohydrodynamic simulations of low-accretion rate supermassive black holes. The modular nature ofMahakalaallows us to quantitatively explore how different regions of the flow influence different image features. We show that most of the emission seen in 1.3 mm images originates close to the black hole and peaks near the photon orbit. We also quantify the relative contribution of the disk, forward jet, and counterjet to 1.3 mm images.
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- PAR ID:
- 10594072
- Publisher / Repository:
- IOP
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 985
- Issue:
- 1
- ISSN:
- 0004-637X
- Page Range / eLocation ID:
- 40
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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