Measuring the F-corona intensity through time correlation of total and polarized visible light images
Bibliographic record
Abstract
We present a new correlation method for deriving the F-corona intensity distribution, which is based on the analysis of the evolution of the total and polarized visible light (VL) images. We studied the one-month variation profiles of the total and polarized brightness acquired with Large Angle Spectrometric COronagraph and found that in some regions they are highly correlated. Assuming that the F-corona does not vary significantly on a timescale of one month, we estimated its intensity in the high-correlation regions and reconstructed the corresponding intensity maps both during the solar-minimum and solar-maximum periods. Systematic uncertainties were estimated by performing dedicated simulations. We compared the resulting F-corona images with those determined using the inversion technique and found that the correlation method provides a smoother intensity distribution. We also obtained that the F-corona images calculated for consecutive months show no significant variation. Finally, we note that this method can be applied to the future high-cadence VL observations carried out with the Metis/Solar Orbiter coronagraph.
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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 | 0.001 |
| 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 | 0.000 | 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".