Globular Cluster UVIT Legacy Survey (GlobULeS) – I. FUV–optical colour–magnitude diagrams for eight globular clusters
Bibliographic record
Abstract
ABSTRACT We present the first results of eight globular clusters (GCs) from the AstroSat/UVIT Legacy Survey programme GlobULeS based on the observations carried out in two far-ultraviolet (FUV) filters (F148W and F169M). The FUV–optical and FUV–FUV colour–magnitude diagrams (CMDs) of GCs with the proper motion membership were constructed by combining the Ultra-Violet Imaging Telescope (UVIT) data with Hubble Space Telescope (HST) UV Globular Cluster Survey data for inner regions and Gaia Early Data Release for regions outside the HST’s field. We detect sources as faint as F148W ∼ 23.5 mag, which are classified based on their locations in CMDs by overlaying stellar evolutionary models. The CMDs of eight GCs are combined with the previous UVIT studies of three GCs to create stacked FUV–optical CMDs to highlight the features/peculiarities found in the different evolutionary sequences. The FUV (F148W) detected stellar populations of 11 GCs comprise 2816 horizontal branch (HB) stars [190 extreme HB (EHB) candidates], 46 post-HB (pHB), 221 blue straggler stars (BSSs), and 107 white dwarf (WD) candidates. We note that the blue HB colour extension obtained from F148W − G colour and the number of FUV detected EHB candidates are strongly correlated with the maximum internal helium (He) variation within each GC, suggesting that the FUV–optical plane is the most sensitive to He abundance variations in the HB. We discuss the potential science cases that will be addressed using these catalogues including HB morphologies, BSSs, pHB, and WD stars.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".