Composite Magnitude: A tool for creating better star tracker catalogs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a process for building star tracker star catalogs with the aim of improving photometric predictability, reducing the required number of calibration images, and increasing the sensor availability. We introduce a technique to predict the apparent brightness of stars as perceived by imaging optical sensors such as star trackers. This prediction method uses a small set of reference stars with average measured brightness to estimate the apparent brightness of all other stars. This approach relies on the calculation of a brightness metric called composite magnitude. This metric is similar to visual magnitude but also incorporates the photometric responses of the star in the UBVRI Johnson-Cousins system, optical properties of the star tracker lens, and the quantum efficiency of the pixel detector. Furthermore, we can use the method presented to predict performance of the star catalogs when lens or detector properties are modified during the star tracker development. The presented brightness prediction technique is validated by on-orbit and ground test telemetry data.
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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