The Ultracool SpeXtroscopic Survey. I. Volume-limited Spectroscopic Sample and Luminosity Function of M7−L5 Ultracool Dwarfs
Bibliographic record
Abstract
Abstract We present a volume-limited, spectroscopically verified sample of M7−L5 ultracool dwarfs (UCDs) within 25 pc. The sample contains 410 sources, of which 93% have trigonometric distance measurements (80% from Gaia DR2) and 81% have low-resolution (R ∼ 120), near-infrared (NIR) spectroscopy. We also present an additional list of 60 sources that may be M7−L5 dwarfs within 25 pc when distance or spectral-type uncertainties are taken into account. The spectra provide NIR spectral and gravity classifications, and we use these to identify young sources, red and blue J − K S color outliers, and spectral binaries. We measure very low gravity and intermediate-gravity fractions of and , respectively; fractions of red and blue color outliers of and , respectively; and a spectral binary fraction of . We present an updated luminosity function for M7−L5 dwarfs continuous across the hydrogen-burning limit that agrees with previous studies. We estimate our completeness to range between 69% and 80% when compared to an isotropic model. However, we find that the literature late-M sample is severely incomplete compared to L dwarfs, with completeness of and , respectively. This incompleteness can be addressed with astrometric-based searches of UCDs with Gaia to identify objects previously missed by color- and magnitude-limited surveys.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".