In-flight star calibration and simulation methodology for the Metis coronagraph on board solar orbiter
Bibliographic record
Abstract
Stellar in-flight calibration plays a pivotal role in improving the reliability of scientific data acquired by space optical instruments. Changes in sensitivity and performance of the image quality, caused by factors such as optical component degradation or misalignment, can be discovered and tracked by employing in-flight star images and comparing them with on-ground measurements. In this work, we introduce two simulation processes useful for this purpose and apply them to the Metis coronagraph aboard the ESA/NASA Solar Orbiter spacecraft. The first simulation process is a methodology for predicting star visibility in the Field of View (FoV) of the instrument. The second one improves the former code, integrating characteristics on the source, such as star magnitude, and the instrumental features, including reflectivity/transmission of the optical elements, and detector characteristics, e.g. bias, dark current,... The ultimate aim of the simulation is to generate an estimation of the intensity, in Digital Numbers, expected for each pixel of the detector, thus offering valuable insights into the instrument’s response to varying input flux conditions. This innovative approach will provide a comprehensive tool to anticipate and understand the coronagraph’s behavior in response to different celestial scenarios (e.g. from minimum to solar maximum conditions), contributing to more effective in-flight calibrations. Indeed, Metis operates in proximity to the Sun, in a challenging environment marked by high temperatures and significant temperature variations. Although the current results are preliminary, further work is needed to refine and fully understand the simulation outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".