Follow-up of Young Stars Identified with BANYAN Σ: New Low-mass Members of Nearby Moving Groups
Bibliographic record
Abstract
Abstract The characterization of moving groups offers a powerful means to identify large populations of young stars. In this paper, we present a sample of follow-up observations for 56 systems that have previously been proposed as members of young stellar associations through the application of the BANYAN Σ kinematic classification tool. Our measurements, which probe seven different associations, provide a sample of 39 stellar systems that either are confirmed or appear consistent with being young members of their respective associations. Nineteen of these are single M dwarfs. This sample expands our knowledge of Upper Centaurus Lupus, Coma Berenices, and AB Doradus Major to cooler temperatures and also significantly increases the known population of the Carina-Near association. The young systems present excellent targets for future planet searches and would also be valuable for studies of star formation and evolution. Additionally, we find two stellar systems that show indications of being rare instances of late-stage circumstellar accretion. Lastly, our follow-up measurements serve as a test of BANYAN Σ, finding an overall contamination rate that is consistent with previous findings (29% for systems with RV measurements, 37% without).
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".