Imaging the Schwarzschild-radius-scale Structure of M87 with the Event Horizon Telescope Using Sparse Modeling

By K. Akiyama, K. Kuramochi, Shiro Ikeda, Vincent L. Fish, F. Tazaki, M. Honma, Sheperd S. Doeleman, Avery E. Broderick, J. Dexter, M. Mościbrodzka and 3 more. 2017

142 citations recorded by the source corpus.

We propose a new imaging technique for radio and optical/infrared interferometry. The proposed technique reconstructs the image from the visibility amplitude and closure phase, which are standard data products of short-millimeter very long baseline interferometers such as the Event Horizon Telescope (EHT) and optical/infrared interferometers, by utilizing two regularization functions: the ℓ1-norm and total variation (TV) of the brightness distribution. In the proposed method, optimal regularization parameters, which represent the sparseness and effective spatial resolution of the image, are derived from data themselves using cross-validation (CV). As an application of this technique, we present simulated observations of M87 with the EHT based on four physically motivated models. We confirm that ℓ1 + TV regularization can achieve an optimal resolution of ∼20%–30% of the diffraction limit λ/Dmax, which is the nominal spatial resolution of a radio interferometer. With the proposed technique, the EHT can robustly and reasonably achieve super-resolution sufficient to clearly resolve the black hole shadow. These results make it promising for the EHT to provide an unprecedented view of the event-horizon-scale structure in the vicinity of the supermassive black hole in M87 and also the Galactic center Sgr A*.

Subfields: Astronomy and Astrophysics

View Paper: https://www.semanticscholar.org/paper/a18fdc9258dac8ff614291efbe2877fa32e840a2

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In Astronomy and Astrophysics

Citations in the corpus

First Sagittarius A* Event Horizon Telescope Results. IV. Variability, Morphology, and Black Hole Mass (2022, methodology): “…one sampling-based posterior exploration algorithm (THEMIS; Broderick et al. 2020a, 2020b), one CLEAN-based deconvolution algorithm (DIFMAP; Shepherd 1997), and two “regularized maximum likelihood” (RML) algorithms (eht-imaging,Chael et al. 2016, 2018; SMILI, Akiyama et al. 2017a, 2017b).”