A prototype of the UV detector for METIS on Solar Orbiter
Bibliographic record
Abstract
METIS (Multi Element Telescope for Imaging and Spectroscopy) is one of the instruments included in the science payload of the ESA mission Solar Orbiter: a coronograph able to perform broadband polarization imaging in the visible range, and narrow band imaging in UV (HI Lyman-α) and EUV (HeII Lyman-α). In addition, it will acquire spectra of the solar corona simultaneously to UV/EUV imaging. It will be equipped with two detectors: a hybrid APS dedicated to the visible channel and an Intensified APS for the UV/EUV channel. The spectroscopic channel will share the same detector as the UV/EUV corona imaging, with the spectrum imaged on a portion of the detector not used by the corona image. We present the development of the UV/EUV detector consisting of a CMOS APS imaging device to be coupled with a microchannel plate intensifier. Other than constraints related to the harsh environment (radiation, temperature, visible stray-light), the METIS UV detector has the additional challenge of managing different count rates associated with the three different kind of measurements (UV imaging, EUV imaging and spectroscopy). The required dynamic range is further extended since observations will be planned at different distances from the Sun, varying image scale over a fixed vignetting function. We will present the architecture of this UV detector, describing the prototype developed in order to optimize the performance on the overall dynamic range required by METIS.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".