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1. Principal-component Interferometric Modeling (PRIMO), an Algorithm for EHT Data. I. Reconstructing Images from Simulated EHT Observations

   https://doi.org/10.3847/1538-4357/acaa9a  

   Medeiros, Lia; Psaltis, Dimitrios; Lauer, Tod R.; Özel, Feryal (February 2023, The Astrophysical Journal)

   Abstract The sparse interferometric coverage of the Event Horizon Telescope (EHT) poses a significant challenge for both reconstruction and model fitting of black hole images.PRIMOis a new principal components analysis-based algorithm for image reconstruction that uses the results of high-fidelity general relativistic, magnetohydrodynamic simulations of low-luminosity accretion flows as a training set. This allows the reconstruction of images that are consistent with the interferometric data and that live in the space of images that is spanned by the simulations.PRIMOfollows Monte Carlo Markov Chains to fit a linear combination of principal components derived from an ensemble of simulated images to interferometric data. We show thatPRIMOcan efficiently and accurately reconstruct synthetic EHT data sets for several simulated images, even when the simulation parameters are significantly different from those of the image ensemble that was used to generate the principal components. The resulting reconstructions achieve resolution that is consistent with the performance of the array and do not introduce significant biases in image features such as the diameter of the ring of emission. 

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2. Theoretical Foundation of Black Hole Image Reconstruction Using PRIMO

   https://doi.org/10.3847/1538-4357/ada60f  

   Psaltis, Dimitrios; Özel, Feryal; Medeiros, Lia; Lauer, Tod R (April 2025, The Astrophysical Journal)

   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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3. VLBInet: Radio Interferometry Data Classification for EHT with Neural Networks

   Lin, Joshua Y.-Y.; Pesce, Dominic W.; Wong, George N.; Arasanipalai, Ajay Uppili; Prather, Ben S.; Gammie, Charles F. (October 2021, ArXivorg)

   The Event Horizon Telescope (EHT) recently released the first horizon-scale images of the black hole in M87. Combined with other astronomical data, these images constrain the mass and spin of the hole as well as the accretion rate and magnetic flux trapped on the hole. An important question for the EHT is how well key parameters, such as trapped magnetic flux and the associated disk models, can be extracted from present and future EHT VLBI data products. The process of modeling visibilities and analyzing them is complicated by the fact that the data are sparsely sampled in the Fourier domain while most of the theory/simulation is constructed in the image domain. Here we propose a data- driven approach to analyze complex visibilities and closure quantities for radio interferometric data with neural networks. Using mock interferometric data, we show that our neural networks are able to infer the accretion state as either high magnetic flux (MAD) or low magnetic flux (SANE), suggesting that it is possible to perform parameter extraction directly in the visibility domain without image reconstruction. We have applied VLBInet to real M87 EHT data taken on four di↵erent days in 2017 (April 5, 6, 10, 11), and our neural networks give a score prediction 0.52, 0.4, 0.43, 0.76 for each day, with an average score 0.53, which shows no significant indication for the data to lean toward either the MAD or SANE state. 

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4. VLBInet: Radio Interferometry Data Classification for EHT with Neural Networks

   Lin, Yao_Yu; Pesce, Dominic W; Wong, George N; Arasanipalai, Ajay U; Prather, Ben S; Gammie, Charles F (October 2021, arXiv)

   The Event Horizon Telescope (EHT) recently released the first horizon-scale images of the black hole in M87. Combined with other astronomical data, these images constrain the mass and spin of the hole as well as the accretion rate and magnetic flux trapped on the hole. An important question for the EHT is how well key parameters, such as trapped magnetic flux and the associated disk models, can be extracted from present and future EHT VLBI data products. The process of modeling visibilities and analyzing them is complicated by the fact that the data are sparsely sampled in the Fourier domain while most of the theory/simulation is constructed in the image domain. Here we propose a data-driven approach to analyze complex visibilities and closure quantities for radio interferometric data with neural networks. Using mock interferometric data, we show that our neural networks are able to infer the accretion state as either high magnetic flux (MAD) or low magnetic flux (SANE), suggesting that it is possible to perform parameter extraction directly in the visibility domain without image reconstruction. We have applied VLBInet to real M87 EHT data taken on four different days in 2017 (April 5, 6, 10, 11), and our neural networks give a score prediction 0.52, 0.4, 0.43, 0.76 for each day, with an average score 0.53, which shows no significant indication for the data to lean toward either the MAD or SANE state. 

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5. Multifrequency Black Hole Imaging for the Next-generation Event Horizon Telescope

   https://doi.org/10.3847/1538-4357/acb7e4  

   Chael, Andrew; Issaoun, Sara; Pesce, Dominic W.; Johnson, Michael D.; Ricarte, Angelo; Fromm, Christian M.; Mizuno, Yosuke (March 2023, The Astrophysical Journal)

   Abstract The Event Horizon Telescope (EHT) has produced images of the plasma flow around the supermassive black holes in Sgr A* and M87* with a resolution comparable to the projected size of their event horizons. Observations with the next-generation Event Horizon Telescope (ngEHT) will have significantly improved Fourier plane coverage and will be conducted at multiple frequency bands (86, 230, and 345 GHz), each with a wide bandwidth. At these frequencies, both Sgr A* and M87* transition from optically thin to optically thick. Resolved spectral index maps in the near-horizon and jet-launching regions of these supermassive black hole sources can constrain properties of the emitting plasma that are degenerate in single-frequency images. In addition, combining information from data obtained at multiple frequencies is a powerful tool for interferometric image reconstruction, since gaps in spatial scales in single-frequency observations can be filled in with information from other frequencies. Here we present a new method of simultaneously reconstructing interferometric images at multiple frequencies along with their spectral index maps. The method is based on existing regularized maximum likelihood (RML) methods commonly used for EHT imaging and is implemented in theeht-imagingPython software library. We show results of this method on simulated ngEHT data sets as well as on real data from the Very Long Baseline Array and Atacama Large Millimeter/submillimeter Array. These examples demonstrate that simultaneous RML multifrequency image reconstruction produces higher-quality and more scientifically useful results than is possible from combining independent image reconstructions at each frequency. 

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