Shape and spin-state model of tumbling asteroid (319) Leona
Bibliographic record
Abstract
Context. Stellar occultations by asteroids observed from several stations are routinely used to reconstruct the asteroid’s sky-plane projections. Together with the asteroid’s shape model reconstructed from photometry, such occultations enable us to precisely determine its size and reveal details of its shape. When reducing occultation timings, the usual assumption is that the star has a negligible angular diameter compared to the asteroid, so it is modeled as a point source. The occultation of Betelgeuse (α Orionis) by asteroid (319) Leona on 12 December 2023 was a rare exception – the apparent angular diameter of the star was ∼50 mas, about the same as that of the asteroid. Aims. This work aimed to reconstruct the shape and spin model of asteroid Leona. Then, the projection of that model for the time of the occultation can be computed, which is necessary to interpret the occultation timings and infer valuable information about Betelgeuse itself. Methods. We collected available photometric data of Leona, carried out new observations, and reconstructed a unique convex shape model. Using three other occultations observed in 2023, we scaled this convex model. We also reconstructed an alternative nonconvex model with the same spin parameters and size but showing some surface details that explain better one of the occultations. Results. We confirmed the tumbling rotation state of Leona and uniquely determined the rotation period Pψ = 1172.2 ± 0.3 h and the precession period Pϕ = 314.27 ± 0.02 h. The volume-equivalent diameter determined by occultations is 59.1 ± 0.9 km. The reconstructed shape model of Leona enabled us to compute its sky-plane projection for the time of the Betelgeuse occultation. Conclusions. A reliable shape model with accurate dimensions and accurate rotation and precession periods has been reconstructed for slowly tumbling asteroid Leona. It can be used to interpret the observed occultation of Betelgeuse by Leona.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".