Improving Performance Of Star Trackers: Brightness Prediction And Star Centroid Accuracy
Bibliographic record
Abstract
<p>This research presents two strategies to improve the performance of star trackers for nanosatellite applications. The first strategy is the development of a brightness prediction model with the aim of improving the star catalogue selection during the early stages of star tracker development. This brightness prediction model reduces the need for high number of calibration images, and relies on calculation of fractional responses in the Johnson-Cousins U,B,V,R,I passbands. This method shows an improvement in the photometric predictions by a brightness magnitude order of 0.4 compared to the standard visible magnitudes from reference catalogues. The second presented strategy focuses on the star centroid detection. A centroid refinement method is implemented with aim of improving the centroid accuracy while reducing the effect of random noise. The effectiveness of this method was investigated by using a set of simulated and real images, and centroid errors were found to be lower than 0.5 pixels.</p>
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".