First Determination of 2D Speed Distribution within the Bodies of Coronal Mass Ejections
Bibliographic record
Abstract
The determination of the speed of Coronal Mass Ejections (CMEs) is usually done by tracking brighter features (such as the CME front and core) in visible light coronagraphic images and by deriving unidimensional profiles of the CME speed as a function of altitude or time. Nevertheless, CMEs are usually characterized by the presence of significant density inhomogeneities propagating outward with different radial and latitudinal projected speeds, resulting in a complex evolution eventually forming the Interplanetary CME. In this work, we demonstrate for the first time how coronagraphic image sequences can be analyzed to derive 2D maps of the almost instantaneous plasma speed distribution within the body of CMEs. The technique is first tested with the analysis of synthetic data, and then applied to real observations. Results from this work allow to characterize the distribution and time evolution of kinetic energy inside CMEs, as well as the mechanical energy (combined with the kinetic and potential energy) partition between the core and front of the CME. In the future, CMEs will be observed by two channels (VL and UV Ly-$\alpha$) coronagraphs, such as Metis on-board ESA Solar Orbiter mission as well as Ly-$\alpha$ Solar Telescope (LST) on-board Chinese Advanced Space-based Solar Observatory (ASO-S) mission. Our results will help the analysis of these future observations, helping in particular to take into account the 2D distribution of Ly-$\alpha$ Doppler dimming effect.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".