Deficit of Wide Binaries in the η Chamaeleontis Young Cluster
Bibliographic record
Abstract
We have carried out a sensitive high-resolution imaging survey of stars in the young (6-8 Myr), nearby (97 pc) compact cluster around η Chamaeleontis to search for stellar and substellar companions. Our data were obtained using the NACO adaptive optics system on the ESO Very Large Telescope (VLT). Given its youth and proximity, any substellar companions are expected to be luminous, especially in the near-infrared, and thus easier to detect next to their parent stars. Here, we present VLT NACO adaptive optics imaging with companion detection limits for 17 η Cha cluster members, and follow-up VLT ISAAC near-infrared spectroscopy for companion candidates. The widest binary detected is ~0 2, corresponding to the projected separation 20 AU, despite our survey being sensitive down to substellar companions outside 0 3, and planetary-mass objects outside 0 5. This implies that the stellar companion probability outside 0 3 and the brown dwarf companion probability outside 0 5 are less than 0.16 with 95% confidence. We compare the wide binary frequency of η Cha to that of the similarly aged TW Hydrae association and estimate the statistical likelihood that the wide binary probability is equal in both groups to be less than 2 × 10 -4 . Even though the η Cha cluster is relatively dense, stellar encounters in its present configuration cannot account for the relative deficit of wide binaries. We thus conclude that the difference in wide binary probability in these two groups provides strong evidence for multiplicity properties being dependent on environment. In two appendices we derive the projected separation probability distribution for binaries, used to constrain physical separations from observed projected separations, and summarize statistical tools useful for multiplicity studies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".