Simulations for in-flight stellar calibration aimed at monitoring space instruments optical performance
Bibliographic record
Abstract
Abstract. Stellar in-flight calibrations have a relevant impact on the ability of space optical instruments, such as telescopes or cameras, to provide reliable scientific products, i.e. accurate calibrated data. Indeed, by using the in-flight star images, the instrument optical performance can be checked and compared with the on-ground measurements. The results of the analysis of star images, throughout the whole instrument lifetime in space, will allow tracking the changes in instrument performance and sensitivity due to optical components degradation or misalignment. In this paper we present the concept, the necessary input and the available outputs of the simulations performed to predict the stars visible in the FoV of a specific space instrument. As an example of the method, its application to two specific cases, i.e, Metis coronagraph on-board Solar Orbiter and the stereo camera STC on-board BepiColombo, will be given. Indeed, due to their operation in proximity to the Sun, and also to Mercury for STC, both instruments operate in a hostile environment, are subjected to high temperatures and experience high temperature variations. Performance optical monitoring is thus extremely important.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".