Black Boxes in Black Hole Imaging
Juliusz Doboszewski, Jamee Elder
physics.hist-ph, astro-ph.HE, astro-ph.IM, gr-qc
Submitted: 2026-07-02
Comments: forthcoming in Synthese, TC: The Philosophy of Experiments
License: http://creativecommons.org/licenses/by/4.0/
The gist: We investigate the epistemic opacity of computer simulations and machine learning methods in the context of black hole imaging.
Terminology
Abstract
We investigate the epistemic opacity of computer simulations and machine learning methods in the context of black hole imaging. We argue that there are forms of opacity-including opacity resulting from the use of machine learning-which do not need to affect the reliability of an inference when it is seen as a part of a broader inferential framework. We propose conditions under which that can plausibly be the case, and discuss how opaque methods can be useful in the context of the (next generation) Event Horizon Telescope. However, we also argue that at least one problematic form of opacity is currently present in black hole imaging: GRMHD models of Sagittarius A* are opaque. This form of opacity signals the limitations of current understanding of the models of this source, and constrains the potential uses of ML models in future observations.
Sources
- The R2D2 deep neural network series paradigm for fast precision imaging in radio astronomy
- The Benchmarking Epistemology: Validity Theory for Evaluating Machine Learning Models
- A Native Hawaiian-led summary of the current impact of constructing the Thirty Meter Telescope on Maunakea
- VLBInet: Radio Interferometry Data Classification for EHT with Neural Networks
- Parameter Inference of Black Hole Images using Deep Learning in Visibility Space
- Theoretical Foundation of Black Hole Image Reconstruction using PRIMO