A 5D view of the <i>α</i> Per, Pleiades, and Praesepe clusters
Bibliographic record
Abstract
Aims . Our scientific goal is to provide revised membership lists of the α Per, Pleiades, and Praesepe clusters exploiting the second data release of Gaia and produce five-dimensional maps ( α , δ , π , μ α cos δ , μ δ ) of these clusters. Methods . We implemented the kinematic method combined with the statistical treatment of parallaxes and proper motions to identify astrometric member candidates of three of the most nearby and best studied open clusters in the sky. Results . We cross-correlated the Gaia catalogue with large-scale public surveys to complement the astrometry of Gaia with multi-band photometry from the optical to the mid-infrared. We identified 517, 1248, and 721 bona fide astrometric member candidates inside the tidal radius of α Per, the Pleiades, and Praesepe, respectively. We cross-matched our final samples with catalogues from previous surveys to address the level of completeness. We update the main physical properties of the clusters, including mean distance and velocity, as well as core, half-mass, and tidal radii. We infer updated ages from the white dwarf members of the Pleiades and Praesepe. We derive the luminosity and mass functions of the three clusters and compare them to the field mass function. We compute the positions in space of all member candidates in the three regions to investigate their distribution in space. Conclusions . We provide updated distances and kinematics for the three clusters. We identify a list of members in the α Per, Pleiades, and Praesepe clusters from the most massive stars all the way down to the hydrogen-burning limit with a higher confidence and better astrometry than previous studies. We produce complete 5D maps of stellar and substellar bona fide members in these three regions. The photometric sequences derived in several colour–magnitude diagrams represent benchmark cluster sequences at ages from 90 to 600 Myr. We note the presence of a stream around the Pleiades cluster extending up to 40 pc from the cluster centre.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".