A homogeneous sample of 34 000 M7−M9.5 dwarfs brighter than
Bibliographic record
Abstract
The space density of late M dwarfs, subtypes M7–M9.5, is not well determined. We applied the photo-type method to iz photometry from the Sloan Digital Sky Survey and YJHK photometry from the UKIRT Infrared Deep Sky Survey, over an effective area of 3070 deg2, to produce a new, bright J(Vega) < 17.5, homogeneous sample of 33 665 M7–M9.5 dwarfs. The typical S/N of each source summed over the six bands is > 100. Classifications are provided to the nearest half spectral subtype. Through a comparison with the classifications in the BOSS Ultracool Dwarfs (BUD) spectroscopic sample, the typing is shown to be accurately calibrated to the BUD classifications and the precision is better than 0.5 subtypes rms; i.e. the photo-type classifications are as precise as good spectroscopic classifications. Sources with large χ2 > 20 include several catalogued late-type subdwarfs. The new sample of late M dwarfs is highly complete, but there is a bias in the classification of rare peculiar blue or red objects. For example, L subdwarfs are misclassified towards earlier types by approximately two spectral subtypes. We estimate that this bias affects only ∼1% of the sources. Therefore the sample is well suited to measure the luminosity function and investigate the softening towards the Galactic plane of the exponential variation of density with height.
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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 | 0.001 | 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 | 0.001 | 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) | 0.003 | 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".