UOCS – XI. Study of blue straggler stars in open cluster NGC 7142 using UVIT/<i>AstroSat</i>
Bibliographic record
Abstract
ABSTRACT We present a study of blue straggler stars (BSSs) of open cluster NGC 7142 using AstroSat/UVIT data and other archival data. Using a machine-learning-based algorithm, ML-MOC, on Gaia DR3 data, we find 546 sources as cluster members. Based on the location on the Gaia colour–magnitude diagram, we identify 10 BSS candidates, also detected in UVIT/F148W filter. We study the variable nature of BSSs by constructing their light curves using the Transiting Exoplanet Survey Satellite data. Two BSSs reported as eclipsing binaries in Gaia DR3 are confirmed to be eclipsing binaries based on our analysis and also show the presence of hot companions as per the multiwavelength spectral energy distributions (SEDs). The physical parameters of the hot companions of these two BSSs derived by fitting binary models to their light curves and those derived from the SEDs are found to be in good agreement. Additionally, five more BSSs in the cluster shows UV excess, four of which are likely to have a hot companion based on SEDs. The hot companions with the estimated temperatures ∼14 000–28 000 K, radii ∼0.01–0.05 R⊙, and luminosities ∼0.03–0.1 L⊙ are inferred to be extremely low-mass (<0.2 M⊙), low-mass (0.2–0.4 M⊙), normal-mass (0.4–0.6 M⊙), and high-mass (>0.6 M⊙) white dwarfs (WDs). For the first time in an open cluster, we find the entire range of masses in WDs found as hot companions of BSSs. These masses imply that the Case-A/Case-B mass transfer and merger are responsible for the formation of at least 60 per cent of the BSSs of 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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".