On-orbit star tracker recalibration: A case study
Why this work is in the frame
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Bibliographic record
Abstract
Star trackers must be calibrated prior to flight so that they can make accurate measurements of star positions within the instrument field of view. This calibration is often performed in atmosphere and once the sensor is launched, it is not uncommon to observe a small shift in some of the calibration parameters. To maximize sensor performance, these parameter values must be corrected to better match the actual observations. In this paper, we explore the several practical strategies for on-orbit recalibration of star trackers. We consider both human-mediated batch-processing as well as autonomous, sequential algorithms. We base our results on orbital data from a number of Sinclair Interplanetary ST16 star trackers launched within the last year. Both approaches to on-orbit calibration can yield significant improvements both to sensor availability and geometric error.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it