Extensive Study of a Coronal Mass Ejection with UV and White-light Coronagraphs: The Need for Multiwavelength Observations
Bibliographic record
Abstract
Abstract Coronal mass ejections (CMEs) often show different features in different bandpasses. By combining data in white-light (WL) and ultraviolet (UV) bands, we have applied different techniques to derive plasma temperatures, electron density, internal radial speed, and so on, within a fast CME. They serve as extensive tests of the diagnostic capabilities developed for the observations provided by future multichannel coronagraphs (such as Solar Orbiter/Metis, Chinese Advanced Space-based Solar Observatory/Lyα Solar Telescope (LST), and PROBA-3/ASPIICS). The data involved include WL images acquired by Solar and Heliospheric Observatory (SOHO)/Large Angle Spectroscopic Coronagraph (LASCO) coronagraphs, and intensities measured by the SOHO/UV Coronagraph Spectrometer (UVCS) at 2.45 R ⊙ in the UV (H i Lyα and O vi 1032 Å lines) and WL channels. Data from the UVCS WL channel have been employed for the first time to measure the CME position angle with the polarization-ratio technique. Plasma electron and effective temperatures of the CME core and void are estimated by combining UV and WL data. Due to the CME expansion and the possible existence of prominence segments, the transit of the CME core results in decreases in the electron temperature down to 105 K. The front is observed as a significant dimming in the Lyα intensity, associated with a line broadening due to plasma heating and flows along the line of sight. The 2D distribution of plasma speeds within the CME body is reconstructed from LASCO images and employed to constrain the Doppler dimming of the Lyα line and simulate future CME observations by Metis and LST.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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 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".