Multi-height probing of horizontal flows in the solar photosphere
Bibliographic record
Abstract
Context. Optical flow methods aim to infer horizontal (transverse, in the general case) velocities in the solar atmosphere from the temporal changes in maps of physical quantities, such as intensity or magnetic field. So far, these methods have mostly been tested and applied to the continuum intensity and line-of-sight (LOS) magnetic field in the low to mid-photosphere. Aims. We tested whether simultaneous spectropolarimetric imaging in two magnetically sensitive optical spectral lines, which probe two different layers of the solar atmosphere (the photosphere and the temperature minimum), can help constrain the depth variation of horizontal flows. Methods. We first tested the feasibility of our method using Fourier local correlation tracking (FLCT) to track physical quantities at different optical depths (log τ 500 = −1, −2, −3, −4) in an atmosphere simulated with the MURaM code. We then inferred the horizontal distribution of the LOS magnetic field component from synthetic spectropolarimetric observations of Fe I 525.0 nm and Mg I b2 spectral lines, applied FLCT to the time sequence of these synthetic magnetograms, and compared our findings with the original height-dependent horizontal velocities. Results. Tracking the LOS magnetic field component (which coincides with the vertical component at the disk center) yields horizontal velocities that, after appropriate temporal and spatial averaging, agree excellently with the horizontal component of the simulated velocities, both calculated at constant τ 500 surfaces, up to the temperature minimum (log τ 500 = −3). When tracking the temperature at constant τ 500 surfaces, this agreement already breaks down completely at the mid photosphere (log τ 500 = −2). Tracking the vertical component of the magnetic field inferred from synthetic observations of the Fe I 525.0 nm and the Mg I b2 spectral lines yields a satisfactory inference of the horizontal velocities in the mid-photosphere (log τ 500 ≈ −1) and the temperature minimum (log τ 500 ≈ −3), respectively. Conclusions. Our results indicate that high-spatial-resolution spectropolarimetric imaging in solar spectral lines can provide meaningful information about the horizontal plasma velocities over a range of heights.
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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 | 0.000 | 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) | 0.001 | 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".