Bibliographic record
Abstract
Context. Messier 35 ( NGC 2168 ) is an important young nearby cluster. Its age, richness and relative proximity make it an ideal target for stellar evolution studies. The Kepler K2 mission recently observed it and provided a high accuracy photometric time series of a large number of sources in this area of the sky. Identifying the cluster’s members is therefore of high importance to optimize the interpretation and analysis of the Kepler K2 data. Aims. We aim to identify the cluster’s members by deriving membership probabilities for the sources within 1° of the cluster’s center, which is farther away than equivalent previous studies. Methods. We measure accurate proper motions and multiwavelength (optical and near-infrared) photometry using ground-based archival images of the cluster. We use these measurements to compute membership probabilities. The list of candidate members from the literature is used as a training set to identify the cluster’s locus in a multidimensional space made of proper motions, luminosities, and colors. Results. The final catalog includes 338 892 sources with multiwavelength photometry. Approximately half (194 452) were detected at more than two epochs and we measured their proper motion and used it to derive membership probability. A total of 4349 candidate members with membership probabilities greater than 50% are found in this sample in the luminosity range between 10 mag and 22 mag. The slow proper motion of the cluster and the overlap of its sequence with the field and background sequences in almost all color–magnitude and color–color diagrams complicate the analysis and the contamination level is expected to be significant. Our study, nevertheless, provides a coherent and quantitative membership analysis of Messier 35 based on a large fraction of the best ground-based data sets obtained over the past 18 years. As such, it represents a valuable input for follow-up studies using, in particular, the Kepler K2 photometric time series.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.035 | 0.018 |
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".