The <i>Gaia</i> ultracool dwarf sample – V: the ultracool dwarf companion catalogue
Bibliographic record
Abstract
ABSTRACT We present the Ultracool Dwarf Companion Catalogue of 278 multiple systems, 32 of which are newly discovered, each with at least one spectroscopically confirmed Ultracool Dwarf, within a 100 pc volume-limited sample. This catalogue is compiled using the Gaia Catalogue of Nearby Stars for stellar primaries and the Gaia Ultracool Dwarf Sample for low-mass companions and includes 241 doubles, 33 triples, and 4 higher-order systems established from positional, proper motion, and parallax constraints. The catalogue seeks to identify probable benchmark systems within 100 pc to obtain model-independent astrophysical parameters of Ultracool Dwarfs. Chance alignment probabilities are calculated to evaluate the physical nature of each system. Astrometric and photometric data from Gaia Data Release 3 and the Two Micron All Sky Survey are included for all objects. We identify potential unseen companions using a combination of the renormalized unit weight error, image parameter determination statistics, non-single star solutions, and photometric blending as provided by Gaia, identifying hierarchical Ultracool triple systems. Our catalogue includes 17 White Dwarf – Ultracool Dwarf systems, whose ages are determined using cooling models. We also use the Gaia FLAME results and the BANYAN Σ procedures to age 40 and 34 systems, respectively, and derive mass estimates from evolutionary models.
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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 | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.005 | 0.006 |
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".