Combing the brown dwarf desert with <i>Gaia</i> DR3
Bibliographic record
Abstract
ABSTRACT We have created an up-to-date catalogue of 214 brown dwarfs (BDs) in binaries with P < 104 d. This allows us to examine the population statistics of the BD desert. We searched Gaia third data release (DR3) non-single star (NSS) results for orbital inclinations of BD candidates, deriving 12 new masses. Three remain as desert BDs whereas nine candidates are found to be low-mass stars. We improved the radial velocity (RV) solutions for three previously studied BD candidates. A further 19 BD masses with periods less than ∼1200 d were identified in the DR3 binary_masses data base. We confirm a valley in the mass distribution with a minimum around 30–35 Mjup, and find that periods <100 d are still underpopulated in comparison with longer periods. The updated mass and eccentricity distribution of BDs still shows a marginally statistically significant split into high- and low-mass BD populations. This hints at two different parent distributions, and two potential origins – either akin to planetary formation, or stellar. There are no low-eccentricity BDs at periods around 100 d. The mass–metallicity distribution of BDs indicates that core accretion is not the dominant formation mechanism for BDs as they do not follow the same trends that giant exoplanets do with metallicity. We identify a diagonal envelope bounding the Gaia BDs in the mass–period plane due to the detection thresholds of the currently available NSS solutions from 34 months of data.
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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 | 0.002 |
| 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) | 0.002 | 0.002 |
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".