FOSSIL. IV. The Significance-convergence Test—An Algorithm for Selecting Reliable Rotation Periods of Small Solar System Bodies
Bibliographic record
Abstract
Abstract Manual review to select a reliable rotation period of small solar system bodies (SSSBs) is a very time-consuming process. With the growing volume of lightcurve data collected by wide-field, high-cadence surveys, such manual inspection has become impractical and unsustainable. In response to this challenge, we present a new algorithm, called the significance-convergence test, which provides a quantitative way to select reliable rotation periods of SSSBs obtained from these wide-field, high-cadence surveys. This algorithm was developed based on two simulations, each containing 162,000 synthetic lightcurves generated according to the observational conditions and data properties of the surveys from the phase I of the Formation of the Outer Solar System: an Icy Legacy (FOSSIL I) and Pan-STARRS 1 (PS1). Using two parameters extracted from period analysis, the successful recoveries of the input rotation periods from the synthetic lightcurves can be distinguished from unsuccessful recoveries and noisy lightcurves. The first parameter, 1/ S , indicates the significance of the best-fit spin rate, while the second parameter, C , represents the condition of convergence of the best-fit lightcurve. This algorithm can also be used as a mapping to the conventional quality code of manual review, U , defined by Warner et al. The significance-convergence test thus provides a practical alternative to manual review, which is a time-consuming and biased process.
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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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".