Strengthening the open cluster distance scale via VVV photometry
Bibliographic record
Abstract
Approximately 14% of known Galactic open clusters possess absolute errors ≤ 20% as evaluated from n ≥ 3 independent distance estimates, and the statistics for age estimates are markedly worse. That impedes such diverse efforts as calibrating standard candles and constraining masses for substellar companions. New data from the VVV survey may be employed to establish precise cluster distances with comparatively reduced uncertainties ( ≤ 10%). This is illustrated by deriving parameters for Pismis 19 and NGC 4349, two pertinent open clusters which hitherto feature sizable uncertainties (60%). Fundamental parameters determined for Pismis 19 from new VVV JHKs photometry are d = 2.40 ± 0.15 kpc, ⟨EJ − H⟩ = 0.34 ± 0.04, and log τ = 9.05 ± 0.10, whereas for NGC 4349 the analysis yielded d = 1.63 ± 0.13 kpc, EJ − H = 0.09 ± 0.02, log τ = 8.55 ± 0.10. The results exhibit a significant ( ≥ 5 × ) reduction in uncertainties, and indicate that: i) existing parameters for the substellar object NGC 4349 127b require revision, in part because the new cluster parameters imply that the host is 20% less-massive (ℳ∗/ℳ⊙ ~ 3.1); ii) R Cru is not a member of NGC 4349 and should be excluded from period-Wesenheit calibrations that anchor the distance scale; iii) and results for Pismis 19 underscore the advantages gleaned from employing deep VVV JHKs data to examine obscured (AV ~ 4) and differentially reddened intermediate-age clusters.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".