Characterization of unexpected anomalies in the Metis UV channel on board Solar Orbiter
Bibliographic record
Abstract
Metis is a solar coronograph, on board the ESA/NASA mission Solar Orbiter, based on a two-channel architecture for broadband polarization imaging in the visible range (580 to 640 nm) and narrow-band imaging in the UV (121.6 nm). The UV channel detector is constituted by an intensified camera equipped with a single-stage microchannel plate (MCP) intensifier coupled to a rad-hard CMOS detector. After the launch on 10 February 2020, some anomalies have been observed in the UV channel data, consisting of unexpected signal variations which are not originated from solar phenomena. In particular, the anomalies appear to be unrelated to the other payload elements, except for the detector itself. Post-processing algorithms have been studied to correct the issue, but a more radical solution is desirable also for the benefit of future missions based on similar MCP detectors. In this work, we investigate a possible origin of the anomalies by exploring the effects of external electric fields on an equivalent detector prototype in a controlled laboratory environment. The obtained results show a significative similarity between the observed variations and the effects of the electric fields on the internal gain of the prototype MCP. A mitigation strategy has been identified by acting on the high voltage bias of the intensifier, and will be soon deployed to the on-orbit instrument. An in-depth analysis of the new acquired data is therefore planned for the next months to verify the impact and the effectiveness of the proposed solution.
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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".