Metallicity, temperature, and gravity scales of M subdwarfs<sup>,</sup>
Bibliographic record
Abstract
Aims. The aim of the project is to define metallicity/gravity/temperature scales for different spectral types of metal-poor M dwarfs. Methods. We obtained intermediate-resolution ultraviolet (R ∼ 3300), optical (R ∼ 5400), and near-infrared (R ∼ 3900) spectra of 43 M subdwarfs (sdM), extreme subdwarfs (esdM), and ultra-subdwarfs (usdM) with the X-shooter spectrograph on the European Southern Observatory Very Large Telescope. We compared our atlas of spectra to the latest BT-Settl synthetic spectral energy distribution over a wide range of metallicities, gravities, and effective temperatures to infer the physical properties for the whole M dwarf sequence (M0–M9.5) at sub-solar metallicities and constrain the latest atmospheric models. Results. The BT-Settl models accurately reproduce the observed spectra across the 450–2500 nm wavelength range except for a few regions. We find that the best fits are obtained for gravities of log (g) = 5.0–5.5 for the three metal classes. We infer metallicities of [Fe/H] = −0.5, −1.5, and −2.0 ± 0.5 dex and effective temperatures of 3700–2600 K, 3800–2900 K, and 3700–2900 K for subdwarfs, extreme subdwarfs, and ultra-subdwarfs, respectively. Metal-poor M dwarfs tend to be warmer by about 200 ± 100 K and exhibit higher gravity than their solar-metallicity counterparts. We derive abundances of several elements (Fe, Na, K, Ca, Ti) for our sample but cannot describe their atmospheres with a single metallicity parameter. Our metallicity scale expands the current scales available for mildly metal-poor planet-host low-mass stars. Our compendium of moderate-resolution spectra covering the 0.45–2.5 micron range represents an important resource for large-scale surveys and space missions to come.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".