Multiplicity among Young Brown Dwarfs and Very Low Mass Stars
Bibliographic record
Abstract
We report on a near-infrared adaptive optics imaging survey of 31 young brown dwarfs and very low mass (VLM) stars, 28 of which are in the Chamaeleon I star-forming region, using the ESO Very Large Telescope. We resolve the suspected 0.16'' (~26 AU) binary Cha Hα 2 and present two new binaries, Hn 13 and CHXR 15, with separations of 0.13'' (~20 AU) and 0.30'' (~50 AU), respectively; the latter is one of the widest VLM systems known. We find a binary frequency of 11 −6 +9 % , thus confirming the trend for a lower binary frequency with decreasing mass. By combining our work with previous surveys, we arrive at the largest sample of young VLM objects (72) with high angular resolution imaging to date. Its multiplicity fraction is in statistical agreement with that for VLM objects in the field. Furthermore, we note that many field stellar binaries with lower binding energies and/or wider cross sections have survived dynamical evolution and that statistical models suggest tidal disruption by passing stars is unlikely to affect the binary properties of our systems. Thus, we argue that there is no significant evolution of multiplicity with age among brown dwarfs and VLM stars in OB and T associations between a few megayears to several gigayears. Instead, the observations so far suggest that VLM objects are either less likely to be born in fragile multiple systems than solar-mass stars or such systems are disrupted very early.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".