CHARACTERIZATION OF THE PRAESEPE STAR CLUSTER BY PHOTOMETRY AND PROPER MOTIONS WITH 2MASS, PPMXL, AND Pan-STARRS
Bibliographic record
Abstract
Membership identification is the first step to determine the properties of a star cluster. Low-mass members in particular could be used to trace the dynamical history, such as mass segregation, stellar evaporation, or tidal stripping, of a star cluster in its Galactic environment. We identified member candidates with stellar masses $\sim$0.11--2.4 Msun of the intermediate-age Praesepe cluster (M44), by using Pan-STARRS and 2MASS photometry, and PPMXL proper motions. Within a sky area of 3 deg radius, 1040 candidates are identified, of which 96 are new inclusions. Using the same set of selection criteria on field stars led to an estimate of a false positive rate 16%, suggesting 872 of the candidates being true members. This most complete and reliable membership list allows us to favor the BT-Settl model in comparison with other stellar models. The cluster shows a distinct binary track above the main sequence, with a binary frequency of 20--40%, and a high occurrence rate of similar mass pairs. The mass function is consistent with that of the disk population but shows a deficit of members below 0.3 solar masses. A clear mass segregation is evidenced, with the lowest-mass members in our sample being evaporated from this disintegrating cluster.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".