A FOURIER OPTICS METHOD FOR CALCULATING STELLAR OCCULTATION LIGHT CURVES BY OBJECTS WITH THIN ATMOSPHERES
Bibliographic record
Abstract
A stellar occultation occurs when a solar system object passes in front of a distant star. The light curves resulting from stellar occultations can reveal many aspects of the obscuring object. For airless bodies, the diffraction light curve specifies the object's size, distance and, if several chords are observed, shape. Occultation light curves are especially sensitive to the presence of atmospheres; the refraction light curve is a function of the atmosphere's density, pressure, and temperature profiles. The goal of this paper is to develop a practical algorithm to model the simultaneous effects of diffraction and refraction for objects in which both phenomena are observable. The algorithm we present is flexible: it can be used to calculate light curves by objects with arbitrary shapes and arbitrary atmospheres (including the presence of opacity sources such as hazes), provided that the atmosphere can be represented by a thin screen with a phase delay and an opacity defined at each location in the screen. Because the algorithm is limited at present to thin atmospheres (in which rays from a star are bent but undergo virtually no translation as they pass through an atmosphere), the gas giants, Earth, Mars, and Venus are not treated. Examples of stellar occultations are presented for round or irregularly shaped objects having thin atmospheres of various column densities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".