Most of what you said is correct but there is a final step you are missing, the image is not entirely constructed from raw data. The interferometry data is sparse and the ‘gaps’ are filled with mathematical solutions from theoretical models, and using statistical models trained on simulation data.
We recently developed PRIMO (Principal-component
Interferometric Modeling; Medeiros et al. 2023a) for in-
terferometric image reconstruction and used it to obtain
a high-fidelity image of the M87 black hole from the 2017
EHT data (Medeiros et al. 2023b). In this approach, we
decompose the image into a set of eigenimages, which
the algorithm “learned” using a very large suite of black-
hole images obtained from general relativistic magneto-
hydrodynamic (GRMHD) simulations
Thanks for sharing that paper. I was indeed missing that information and now agree with your earlier statement.
I think them using magnetohydrodynamical black hole models as a base for the ML is a better approach than standard CLEAN though that the Japanese team used. However, both “only” approach reality.
You’re welcome. I think calling it the output of an ‘AI model’ triggers thoughts of the current generative image models, i.e. entirely fictional which is not accurate, but it is important to recognise the difference between an image and a photo.
I also by no means want to downplay the achievement that the image represents, it’s an amazing result and deserves the praise. Defending criticism and confirming conclusions will always be vital parts of the scientific method.
True, ML and such fell under the umbrella term of AI before, but I feel that with most people using it mostly for LLMs (or things like diffusion models, etc.) right now, it has kinda lost that meaning to some extent…
Most of what you said is correct but there is a final step you are missing, the image is not entirely constructed from raw data. The interferometry data is sparse and the ‘gaps’ are filled with mathematical solutions from theoretical models, and using statistical models trained on simulation data.
Paper: https://arxiv.org/pdf/2408.10322
Thanks for sharing that paper. I was indeed missing that information and now agree with your earlier statement.
I think them using magnetohydrodynamical black hole models as a base for the ML is a better approach than standard CLEAN though that the Japanese team used. However, both “only” approach reality.
You’re welcome. I think calling it the output of an ‘AI model’ triggers thoughts of the current generative image models, i.e. entirely fictional which is not accurate, but it is important to recognise the difference between an image and a photo.
I also by no means want to downplay the achievement that the image represents, it’s an amazing result and deserves the praise. Defending criticism and confirming conclusions will always be vital parts of the scientific method.
True, ML and such fell under the umbrella term of AI before, but I feel that with most people using it mostly for LLMs (or things like diffusion models, etc.) right now, it has kinda lost that meaning to some extent…