Physical parameters of selected <i>Gaia</i> mass asteroids
Bibliographic record
Abstract
Context. Thanks to the Gaia mission, it will be possible to determine the masses of approximately hundreds of large main belt asteroids with very good precision. We currently have diameter estimates for all of them that can be used to compute their volume and hence their density. However, some of those diameters are still based on simple thermal models, which can occasionally lead to volume uncertainties as high as 20–30%. Aims. The aim of this paper is to determine the 3D shape models and compute the volumes for 13 main belt asteroids that were selected from those targets for which Gaia will provide the mass with an accuracy of better than 10%. Methods. We used the genetic Shaping Asteroids with Genetic Evolution (SAGE) algorithm to fit disk-integrated, dense photometric lightcurves and obtain detailed asteroid shape models. These models were scaled by fitting them to available stellar occultation and/or thermal infrared observations. Results. We determine the spin and shape models for 13 main belt asteroids using the SAGE algorithm. Occultation fitting enables us to confirm main shape features and the spin state, while thermophysical modeling leads to more precise diameters as well as estimates of thermal inertia values. Conclusions. We calculated the volume of our sample of main-belt asteroids for which the Gaia satellite will provide precise mass determinations. From our volumes, it will then be possible to more accurately compute the bulk density, which is a fundamental physical property needed to understand the formation and evolution processes of small Solar System bodies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".