Comparison of Deterministic Methods to Estimate Sidereal Rotation Period from Light Curves
Bibliographic record
Abstract
The importance of determining the sidereal rotation period of an astronomical object on future investigations pertaining to said object has been well documented in the literature. Researchers, however, have differed in their techniques used to estimate and model objects in the space catalog. In this paper, several period-estimation methods will be explored ranging across Fourier and phase-folding techniques. These methods will be tested using ground-based observations of light curve data for various resident space objects that fall under a rigid body context (i.e., asteroids, satellites, probes, rocket bodies) and celestial objects like stars and extrasolar planets. The effect of varying sample size, the inadequacies in unevenly sampled data processing, autonomy of the method, and complexity of parameters are investigated. For the models of artificial space objects that are not open source, a simulation is used to generate synthetic light curves with which all of the above-mentioned techniques are also employed. To account for heterogeneity in method parameters, each technique is tested with a range of values to optimize the rotational period. Results for uniformly sampled asteroid data as well as nonuniformly sampled stellar objects and generic sinusoidal data show variances in accuracy of the methods, but certain methods stand out.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".