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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".