Asteroid sizes determined with thermophysical model and stellar occultations
Bibliographic record
Abstract
Context. The sizes of many asteroids, especially slowly rotating, low-amplitude targets, remain poorly constrained due to selection effects. These biases limit the availability of high-quality data, leaving size estimates reliant on spherical shape assumptions. Such approximations introduce significant uncertainties propagating, for example, into density determinations or thermophysical and compositional studies, affecting our understanding of asteroid properties. Aims. This work targets poorly studied main-belt asteroids, for most of which no shape models were previously available. Using only high-quality, dense light curves, thermal infrared observations (systematically including WISE data), and stellar occultations, we aimed to produce reliable shape models and scale them using two independent techniques, allowing for size comparison at the end. We conducted two observing campaigns to achieve this: one to obtain dense photometric light curves and another to acquire multi-chord stellar occultations by these objects. Methods. Shape and spin models were reconstructed using light curve inversion techniques. Sizes were determined via two methods: (1) advanced thermophysical modelling using the convex inversion thermophysical model (CITPM), which optimises spin and shape models to light curve data in the visible range together with infrared data, and (2) scaling the shape models with stellar occultations. Results. We obtained precise sizes and shape models for 15 asteroids. CITPM and occultation-derived sizes agree within 5% for most cases, demonstrating the reliability of the modelling approach. Larger discrepancies are usually linked to incomplete occultation chord coverage. The study also provides insights into surface properties, including albedo, surface roughness and thermal inertia. Conclusions. The use of high-quality data, coupled with an advanced TPM that uses both thermal and visible data while allowing the shape model to be adjusted according to both types of data, enabled us to determine sizes with precision comparable to those derived from multichord stellar occultations. We resolved substantial inconsistencies in previous size determinations for target asteroids, providing good input for future studies on asteroid densities and surface properties.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".