Revisiting the membership, multiplicity, and age of the Beta Pictoris Moving Group in the <i>Gaia</i> era
Bibliographic record
Abstract
ABSTRACT Determining the precise ages of young (tens to a few hundred Myr) kinematic (‘moving’) groups is important for placing star, protoplanetary disc, and planet observations on an evolutionary timeline. The nearby ∼25 Myr-old β Pictoris Moving Group (BPMG) is an important benchmark for studying stars and planetary systems at the end of the primordial disc phase. Gaia DR3 astrometry and photometry, combined with ground-based observations and more sophisticated stellar models, permit a systematic re-evaluation of BPMG membership and age. We combined Gaia astrometry with previously published radial velocities to evaluate moving group membership in a Bayesian framework. To minimize the effect of unresolved stellar multiplicity on age estimates, we identified and excluded multistar systems using Gaia astrometry, ground-based adaptive optics imaging, and multi-epoch radial velocities, as well as literature identifications. We estimated age using isochrone and lithium-depletion-boundary fitting with models that account for the effect of magnetic activity and spots on young, rapidly rotating stars. We find that age estimates are highly model-dependent; Dartmouth magnetic models with ages of 23 ± 8 and 33$^{+9}_{-11}$ Myr provide best fits to the lithium depletion boundary and Gaia MG versus BP–RP colour–magnitude diagram, respectively, whereas a Dartmouth standard model with an age of 11$^{+4}_{-3}$ Myr provides a best fit to the 2-Micron All-Sky Survey-Gaia$M_{K_S}$ versus BP–RP colour–magnitude diagram.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
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".