UOCS –VIII. UV study of the open cluster NGC 2506 using <i>ASTROSAT</i>★
Bibliographic record
Abstract
ABSTRACT We study an intermediate-age open cluster (OC) NGC 2506 using the ASTROSAT/UVIT data and other archival data. We identified 2175 cluster members using a machine learning-based algorithm, ML–MOC, on Gaia EDR3 data. Among the cluster members detected in UVIT filters, F148W, F154W, and F169M, we detect nine blue straggler stars (BSS), three yellow straggler stars (YSS), and three red clump (RC) stars. We construct multiwavelength spectral energy distributions (SEDs) of these objects to characterize them and to estimate their parameters. We discovered hot companions to three BSS, two YSS, and three RC candidates and estimated their properties. The hot companions with estimated temperatures, Teff ∼ 13250–31 000 K, are WDs of extremely low mass (ELM, ∼ 0.20 M⊙), low mass (LM, ∼ 0.20–0.40 M⊙), normal mass (∼ 0.40–0.60 M⊙), and high mass (∼ 0.8 M⊙). We suggest that systems with ELM and LM WDs as companions are formed via Case-A/Case-B mass transfer mechanism. A BSS is the likely progenitor of the high-mass WD, as a star with more than the turn-off mass of the cluster is needed to form a high-mass WD. Thus, systems with a high-mass WD are likely to be formed through merger in triple systems. We conclude that mass transfer as well as merger pathways of BSS formation are present in this cluster.
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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 | 0.001 |
| 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".