Probing Accretion Turbulence in the Galactic Center with EHT Polarimetry
Bibliographic record
Abstract
Abstract Magnetic fields grown by instabilities driven by differential rotation are believed to be essential to accretion onto black holes. These instabilities saturate in a turbulent state; therefore, the spatial and temporal variability in the horizon-resolving images of Sagittarius A* (Sgr A*) will be able to empirically assess this critical aspect of accretion theory. However, interstellar scattering blurs high-frequency radio images from the Galactic center and introduces spurious small-scale structures, complicating the interpretation of spatial fluctuations in the image. We explore the impact of interstellar scattering on the polarized images of Sgr A* and demonstrate that for credible physical parameters, the intervening scattering is non-birefringent. Therefore, we construct a scattering mitigation scheme that exploits horizon-resolving polarized millimeter/submillimeter VLBI observations to generate statistical measures of the intrinsic spatial fluctuations and therefore the underlying accretion flow turbulence. An optimal polarization basis is identified, corresponding to measurements of the fluctuations in magnetic field orientation in three dimensions. We validate our mitigation scheme using simulated data sets and find that current and future ground-based experiments will readily be able to accurately measure the image-fluctuation power spectrum.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".