Thermal properties of slowly rotating asteroids: results from a targeted survey
Bibliographic record
Abstract
Context.Earlier work suggests that slowly rotating asteroids should have higher thermal inertias than faster rotators because the heat wave penetrates deeper into the subsurface. However, thermal inertias have been determined mainly for fast rotators due to selection effects in the available photometry used to obtain shape models required for thermophysical modelling (TPM). Aims.Our aims are to mitigate these selection effects by producing shape models of slow rotators, to scale them and compute their thermal inertia with TPM, and to verify whether thermal inertia increases with the rotation period. Methods.To decrease the bias against slow rotators, we conducted a photometric observing campaign of main-belt asteroids with periods longer than 12 h, from multiple stations worldwide, adding in some cases data from WISE andKeplerspace telescopes. For spin and shape reconstruction we used the lightcurve inversion method, and to derive thermal inertias we applied a thermophysical model to fit available infrared data from IRAS, AKARI, and WISE. Results.We present new models of 11 slow rotators that provide a good fit to the thermal data. In two cases, the TPM analysis showed a clear preference for one of the two possible mirror solutions. We derived the diameters and albedos of our targets in addition to their thermal inertias, which ranged between 3−3+33and 45−30+60J m−2s−1∕2K−1. Conclusions.Together with our previous work, we have analysed 16 slow rotators from our dense survey with sizes between 30 and 150 km. The current sample thermal inertias vary widely, which does not confirm the earlier suggestion that slower rotators have higher thermal inertias.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".