Properties of slowly rotating asteroids from the Convex Inversion Thermophysical Model
Bibliographic record
Abstract
Context. Recent results for asteroid rotation periods from the TESS mission showed how strongly previous studies have underestimated the number of slow rotators, revealing the importance of studying those targets. For most slowly rotating asteroids (those with P > 12 h), no spin and shape model is available because of observation selection effects. This hampers determination of their thermal parameters and accurate sizes. Also, it is still unclear whether signatures of different surface material properties can be seen in thermal inertia determined from mid-infrared thermal flux fitting. Aims. We continue our campaign in minimising selection effects among main belt asteroids. Our targets are slow rotators with low light-curve amplitudes. Our goal is to provide their scaled spin and shape models together with thermal inertia, albedo, and surface roughness to complete the statistics. Methods. Rich multi-apparition datasets of dense light curves are supplemented with data from Kepler and TESS spacecrafts. In addition to data in the visible range, we also use thermal data from infrared space observatories (mainly IRAS, Akari and WISE) in a combined optimisation process using the Convex Inversion Thermophysical Model. This novel method has so far been applied to only a few targets, and therefore in this work we further validate the method itself. Results. We present the models of 16 slow rotators, including two updated models. All provide good fits to both thermal and visible data.The obtained sizes are on average accurate at the 5% precision level, with diameters found to be in the range from 25 to 145 km. The rotation periods of our targets range from 11 to 59 h, and the thermal inertia covers a wide range of values, from 2 to <400 J m−2 s−1∕2 K−1, not showing any correlation with the period. Conclusions. With this work we increase the sample of slow rotators with reliable spin and shape models and known thermal inertia by 40%. The thermal inertia values of our sample do not display a previously suggested increasing trend with rotation period, which mightbe due to their small skin depth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".