http://arxiv.org/abs/2304.06079
We present a new reconstruction of the Event Horizon Telescope (EHT) image of the M87 black hole from the 2017 data set. We use PRIMO, a novel dictionary-learning based algorithm that uses high-fidelity simulations of accreting black holes as a training set. By learning the correlations between the different regions of the space of interferometric data, this approach allows us to recover high-fidelity images even in the presence of sparse coverage and reach the nominal resolution of the EHT array. The black hole image comprises a thin bright ring with a diameter of $41.5\pm0.6\,\mu$as and a fractional width that is at least a factor of two smaller than previously reported. This improvement has important implications for measuring the mass of the central black hole in M87 based on the EHT images.
L. Medeiros, D. Psaltis, T. Lauer, et. al.
Fri, 14 Apr 23
55/64
Comments: 7 pages, 5 figures