UOCS XIV: Study of the Open Cluster NGC 2627 Using UVIT/AstroSat
Bibliographic record
Abstract
Abstract We study the intermediate-age open cluster NGC 2627, located at a distance of ∼2 kpc, using UVIT/AstroSat and other archival data. Using a machine learning-based algorithm, ML-MOC, on the Gaia DR3 data, we identify 422 cluster members, including four blue straggler stars (BSSs), one yellow straggler star (YSS), one blue lurker (BL), one red clump (RC) star, and two binary candidates with detection in both UVIT/F148W and UVIT/F169M filters. We characterise them using multiwavelength spectral energy distributions (SEDs). Out of the above nine sources, one BSS, the BL, and one binary candidate have a source nearby; hence, we did not fit their SEDs. Of the remaining six sources, we successfully fit two with single-component SEDs and four with binary-component SEDs. The binary-component SED-based parameters indicate that the hot companions of BSSs, the YSS, the RC star, and the binary candidate are extremely low-mass white dwarfs, confirming that at least four out of nine stars (44%) are formed via the mass transfer channel. We fit King’s profile function to the high-probability (p > 0.8) cluster members and estimate the cluster core radius (r C ) to be 3.84′ and the tidal radius (r t ) to be 36.85′. We find that the equal-mass binaries are most concentrated towards the cluster center, followed by the single massive stars, and single low-mass stars. The BSS population of the cluster is also found to be located within a radius r ∼ 10 × r C from the cluster center, suggesting the dynamical evolution of the 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".