New Method to Detect and Characterize Active Be Star Candidates in Open Clusters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the aim of better understanding the physical conditions under which Be stars form and evolve, it is imperative to further investigate whether poorly studied young open clusters host Be stars. In this work, we explain how data from Gaia DR2 and DR3 can be combined to recover and characterize active Be stars in open clusters. We test our methodology in four open clusters broadly studied in the literature, known for hosting numerous Be stars. In addition, we show that the disk formation and dissipation approach that is typically used to model long term Be star variability, can explain the observed trends for Be stars in a (GDR3-GDR2) versus GDR3 plot. We propose that extending this methodology to other open clusters, and, in particular, those that are poorly studied, will help to increase the number of Be candidates. Eventually, Be stars may eclipse binary systems in open clusters.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it