UOCS-XII. A study of open cluster NGC 6940 using UVIT/<i>AstroSat</i> cluster properties and exotic populations
Bibliographic record
Abstract
ABSTRACT We study an open cluster NGC 6940 using AstroSat/UVIT (Ultraviolet Imaging Telescope) data and other archival data. This is an intermediate age cluster (∼1 Gyr), located at about 770 pc distance, harbouring several exotic populations apart from normal single and binary stars. We identify members of this cluster using a machine learning algorithm, ML-MOC and identify 492 members, including one blue straggler star (BSS), two yellow straggler stars (YSS), 11 blue lurker (BL) candidates, and two red clump (RC) stars. The cluster shows the effect of mass segregation, with massive stars segregated the most into the cluster, followed by the equal-mass binary members and the single low-mass stars. We report the presence of an extended main-sequence turn-off (eMSTO) feature in this cluster and suggest that the age spread may be a contributing factor behind it. However, the effect of stellar rotation, and the dust absorption needs to be examined more comprehensively with a larger fraction of MSTO stars. All the sixteen sources mentioned above have a counterpart in the UVIT/F169M filter. In order to characterize them, we construct multi-wavelength spectral energy distributions (SEDs) of 14 of these objects having no nearby sources within 3 arcsec. The BSS is successfully fitted with a single-component SED. We find that three BLs, two YSS, and one RC star have UV excess greater than 50 per cent and successfully fit two-component SEDs having hot companions. The parameters derived from the SEDs imply that the hot companions of BLs and the RC star are low-mass and normal-mass white dwarfs, whereas the hot companions of YSS are likely to be subdwarf B stars. We suggest that at least 6 out of 14 stars (∼42 per cent) are formed via mass transfer and/or merger pathways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".