Photometry and astrometry with <i>JWST</i> – III. A NIRCam-Gaia DR3 analysis of the open cluster NGC 2506
Bibliographic record
Abstract
ABSTRACT In the third paper of this series aimed at developing the tools for analysing resolved stellar populations using the cameras on board of the James Webb Space Telescope (JWST), we present a detailed multiband study of the 2 Gyr Galactic open cluster NGC 2506. We employ public calibration data sets collected in multiple filters to: (i) derive improved effective Point Spread Functions (ePSFs) for 10 NIRCam filters; (ii) extract high-precision photometry and astrometry for stars in the cluster, approaching the main sequence (MS) lower mass of ∼0.1 M⊙; and (iii) take advantage of the synergy between JWST and Gaia DR3 to perform a comprehensive analysis of the cluster’s global and local properties. We derived a MS binary fraction of ∼57.5 per cent, extending the Gaia limit (∼0.8 M⊙) to lower masses (∼0.4 M⊙) with JWST. We conducted a study on the mass functions (MFs) of NGC 2506, mapping the mass segregation with Gaia data, and extending MFs to lower masses with the JWST field. We also combined information on the derived MFs to infer an estimate of the cluster present-day total mass. Lastly, we investigated the presence of white dwarfs (WDs) and identified a strong candidate. However, to firmly establish its cluster membership, as well as that of four other WD candidates and of the majority of faint low-mass MS stars, further JWST equally deep observations will be required. We make publicly available catalogues, atlases, and the improved ePSFs.
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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 | 0.000 | 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 | 0.001 |
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".