On the testing and validation of stray light attenuation for microsatellite star tracker baffles
Bibliographic record
Abstract
This thesis describes strategies to perform stray light testing in an earthbound laboratory while accounting for atmospheric and surface scattering phenomena that make these measurements difficult. We present a method to analyze and predict the stray light performance for a baffled star tracker optical system. This method involves a hybrid stray light analysis procedure that combines experimental measurements of a star tracker lens optics and uses ray-tracing to obtain attenuation curves. We demonstrate these analytical techniques using an engineering model ST-16 star tracker from Sinclair Interplanetary along with a baffle prototype. The system attenuation curve's accuracy is validated by comparing independently measured baffle attenuation curves with equivalent ray-tracing models. Additionally, exclusion angles are defined for the ST-16 sensor by calculating the false detection rate that varies with system attenuation levels. These techniques provide a versatile alternative to conventional testing for preliminary design stages for a star tracker baffle that emphasizes the use of modest infrastructure.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".