Observation of solar energetic particles with Metis on board Solar Orbiter on February 25, 2023
Bibliographic record
Abstract
Context.The Solar Orbiter Metis coronagraph captures images of the solar corona in both visible (VL) and ultraviolet (UV) light. Tracks ascribable to the passage of galactic and solar particles appear in the Metis images. An algorithm implemented in the Metis processing electronics allows us to separate the pixels fired by VL photons from those crossed by high-energy particles. These spurious pixels are stored in cosmic-ray matrices that can be visually analyzed for particle monitoring deep into the spacecraft’s interior. This algorithm has been enabled for the VL instrument only, since the process of separating the particle tracks from pixels fired by photons in the UV images was shown to be quite challenging with respect to a quantitative analysis. Aims.This work is aimed at studying galactic cosmic rays (GCRs) and solar energetic particles (SEPs) with the Metis cosmic-ray matrices in February 2023. Methods.We compared a visual analysis of Metis cosmic-ray matrices gathered on February 22, 2023, with GCRs only, and on February 25, 2023 with both GCRs and SEPs, to Monte Carlo simulations of the VL instrument during the same days. Results.We estimated the solar modulation parameter associated with the GCR proton energy spectrum in February 2023. We show that Metis plays the role of monitoring galactic and solar protons. The Metis particle observations are used for the diagnostics of the VL instrument performance and to study the spacecraft inner charging from solar minimum towards the next solar maximum. These achievements have been attained with the benefit of the joint observations of Metis, the Energetic Particle Detector/High Energy Telescope, and near-Earth and Earth-based instruments.
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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 | 0.001 | 0.001 |
| 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".