Temperatures and Metallicities of M Dwarfs in the APOGEE Survey
Bibliographic record
Abstract
M dwarfs have enormous potential for our understanding of structure and formation on both Galactic and exoplanetary scales through their properties and compositions. However, current atmosphere models have limited ability to reproduce spectral features in stars at the coolest temperatures (T eff < 4200 K) and to fully exploit the information content of current and upcoming large-scale spectroscopic surveys. Here we present a catalog of spectroscopic temperatures, metallicities, and spectral types for 5875 M dwarfs in the Apache Point Observatory Galactic Evolution Experiment (APOGEE) and Gaia-DR2 surveys using The Cannon: A flexible, data-driven spectral-modeling and parameter-inference framework demonstrated to estimate stellar-parameter labels, and detailed abundances) to high precision. Using a training sample of 87 M dwarfs with optically derived labels spanning calibrated with bolometric temperatures, and dex calibrated with FGK binary metallicities, we train a two-parameter model with predictive accuracy (in cross-validation) to 77 K and 0.09 dex respectively. We also train a one-dimensional spectral classification model using 51 M dwarfs with Sloan Digital Sky Survey optical spectral types ranging from M0 to M6, to predictive accuracy of 0.7 types. We find Cannon temperatures to be in agreement to within 60 K compared to a subsample of 1702 sources with color-derived temperatures, and Cannon metallicities to be in agreement to within 0.08 dex metallicity compared to a subsample of 15 FGK+M or M+M binaries. Finally, our comparison between Cannon and APOGEE pipeline (ASPCAP DR14) labels finds that ASPCAP is systematically biased toward reporting higher temperatures and lower metallicities for M dwarfs.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".