Abstract
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 ± 0.6 μas and a fractional width that is at least a factor of 2 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.
Original language | English (US) |
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Article number | L7 |
Journal | Astrophysical Journal Letters |
Volume | 947 |
Issue number | 1 |
DOIs | |
State | Published - Apr 1 2023 |
Externally published | Yes |
ASJC Scopus subject areas
- Astronomy and Astrophysics
- Space and Planetary Science