Learning to See: Applying Inverse Recurrent Inference Machines to See through Refractive Scattering
Bibliographic record
Abstract
Abstract The Event Horizon Telescope (EHT) has produced horizon-resolving images of Sagittarius A* (Sgr A*). Scattering in the turbulent plasma of the interstellar medium distorts the appearance of Sgr A* on scales only marginally smaller than the fiducial resolution of EHT. The scattering process both diffractively blurs and adds stochastic refractive substructures that limits the practical angular resolution of EHT images of Sgr A*. We explore the ability of a novel recurrent neural network machine learning framework to mitigate these scattering effects, after training on sample data that are agnostic to general relativistic magnetohydrodynamics (GRMHD). We demonstrate that if instrumental limitations are negligible, it is possible to nearly completely mitigate interstellar scattering at a wavelength of 1.3 mm. We validate and quantify the fidelity of this scattering mitigation scheme with physically relevant GRMHD simulations. We find that we can accurately reconstruct resolved structures at the scale of 3 μas, well below the nominal instrumental resolution of EHT, 24 μas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".