Combining white light and UV Lyman-<i>α</i>coronagraphic images to determine the solar wind speed
Bibliographic record
Abstract
Context. The availability of multi-channel coronagraphic images in different wavelength intervals acquired from the space will provide a new view of the solar corona, allowing us to investigate the 2D distribution and time evolution of many plasma physical parameters, such as plasma density, temperature, and outflow speed. Aims. This work focuses on the combination of white light (WL) and UV (Ly α ) coronagraphic images to demonstrate the capability of measuring the solar wind speed in the inner corona directly with the ratio of these two images (a technique called the quick inversion method), thus avoiding having to account for the line-of-sight (LOS) integration effects in the inversion of data. Methods. After a derivation of the theoretical basis and illustration of the main hypotheses in the quick inversion method, the data inversion technique is tested first with 1D radial analytic profiles and then with 3D numerical MHD simulations in order to show the effects of variabilities related to different phases of the solar activity cycle and the complex LOS distribution of plasma parameters. The same technique is also applied to average WL and UV images obtained from real data acquired by the SOHO UVCS and LASCO instruments around the minimum and maximum of the solar activity cycle. Results. Comparisons between input and output velocities show a good agreement overall, demonstrating that this method, which allowed us to infer the solar wind speed with the WL-to-UV image ratio, can be complementary to more complex techniques requiring the full LOS integration. The analysis described here also allowed us to quantify the possible errors in the outflow speed, and to identify the coronal regions where the quick inversion method performs at the best. The quick inversion applied to real UVCS and LASCO data also allowed us to reconstruct the typical bimodal distribution of fast and slow wind at solar minimum, and to derive a more complex picture around the solar maximum. Conclusions. The application of the technique shown here will be very important for the future analyses of data acquired with multi-channel WL and UV (Ly α ) coronagraphs, such as Metis on board the Solar Orbiter, LST on board ASO-S, and any other future WL and UV Ly α multi-channel coronagraphs.
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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".