The EB Correlation in Resolved Polarized Images: Connections to the Astrophysics of Black Holes
Bibliographic record
Abstract
Abstract We present an in-depth analysis of the newly proposed correlation function in visibility space, between the E and B modes of linear polarization, hereafter the EB correlation, for a set of time-averaged general relativistic magnetohydrodynamical simulations compared with the phase map from different semianalytic models and the Event Horizon Telescope (EHT) 2017 data for M87*. We demonstrate that the phase map of time-averaged EB correlation contains novel information that might be linked to black hole (BH) spin, accretion state, and electron temperature. A detailed comparison with a semianalytic approach with different azimuthal expansion modes shows that to recover the morphology of real/imaginary part of the correlation function and its phase, we require higher orders of azimuthal modes. To extract the phase features, we use Zernike polynomial reconstruction developing an empirical metric to break degeneracies between models with different BH spins that are qualitatively similar. We use a set of geometrical ring models with various magnetic and velocity field morphologies, showing that both the image space and visibility-based EB-correlation morphologies in magnetically arrested disk simulations can be explained with simple fluid and magnetic field geometries as used in ring models. Standard and normal evolutions by contrast are harder to model, demonstrating that the simple fluid and magnetic field geometries of ring models are not sufficient to describe them owing to higher Faraday rotation depths. A qualitative comparison with the EHT data demonstrates that some of the features in the phase of EB correlation might be well explained by the current models for BH spins and electron temperatures, while others require larger theoretical surveys.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".