2023-04-13 ジョージア工科大学
PRIMOは、辞書学習と呼ばれる機械学習の一部であり、コンピュータが大量のトレーニング資料に基づいてルールを生成できるようにする。PRIMOを使用すると、コンピュータは、30,000以上の高精度シミュレーション画像を分析し、画像構造の共通パターンを見つけ、EHT観測の高度に正確な表現を提供し、同時に画像の欠損構造の高精度な推定値を提供する。
＜関連情報＞
- https://research.gatech.edu/sharper-look-m87-black-hole
- https://iopscience.iop.org/article/10.3847/2041-8213/acc32d
- https://iopscience.iop.org/article/10.3847/1538-4357/acaa9a/meta
PRIMOで再構成されたM87ブラックホール像 The Image of the M87 Black Hole Reconstructed with PRIMO
Lia Medeiros, Dimitrios Psaltis, Tod R. Lauer and Feryal Özel
The Astrophysical Journal Letters Published 2023 April 13
DOI:10.3847/2041-8213/acc32d
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.
EHTデータのためのアルゴリズム「主成分干渉モデリング（PRIMO）」。I. EHTの模擬観測データからの画像再構成 Principal-component Interferometric Modeling (PRIMO), an Algorithm for EHT Data. I. Reconstructing Images from Simulated EHT Observations
Lia Medeiros, Dimitrios Psaltis, Tod R. Lauer and Feryal Öze
The Astrophysical Journal Letters Published 2023 February 3
DOI:10.3847/1538-4357/acaa9a
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. PRIMO is 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. PRIMO follows Monte Carlo Markov Chains to fit a linear combination of principal components derived from an ensemble of simulated images to interferometric data. We show that PRIMO can 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.
