Predicting metallicities and carbon abundances from <i>Gaia</i> XP spectra for (carbon-enhanced) metal-poor stars
Bibliographic record
Abstract
ABSTRACT Carbon-rich (C-rich) stars can be found at all metallicities and evolutionary stages. They are often the result of mass transfer from a companion, but some of the most metal-poor C-rich objects are likely carrying the imprint of the metal-free First Stars from birth. In this work, we employ a neural network to predict metallicities and carbon abundances for over 10 million stars with Gaia low-resolution XP spectra, down to $\rm {[Fe/H]} = -3.0$ and up to $\rm {[C/Fe]} \approx +2$. We identify ${\sim} 2000$ high-confidence bright ($G\lt 16$) carbon-enhanced metal-poor stars with $\rm {[Fe/H]} \lt -2.0$ and $\rm {[C/Fe]} \gt +0.7$. The majority of our C-rich candidates have $\rm {[Fe/H]} \gt -2.0$ and are expected to be binary mass-transfer products, supported by high barium abundances in the GALAH (GALactic Archaeology with HERMES) survey and/or their Gaia Renormalised Unit Weight Error (RUWE) and radial velocity variations. We confirm previous findings of an increase in C-rich stars with decreasing metallicity, adopting a definition of $3\sigma$ outliers from the [C/Fe] distribution, although our frequency appears to flatten for $-3.0 \lt \rm {[Fe/H]} \lt -2.0$ at a level of $6\!\!-\!\!7{{\ \rm per\, cent}}$. We also find that the fraction of C-rich stars is low among globular cluster stars (connected to their lower binary fraction), and that it decreases for field stars more tightly bound to the Milky Way. We interpret these last results as evidence that disrupted globular clusters contribute more in the inner Galaxy, supporting previous work. Homogeneous samples such as these are key to understanding the full population properties of C-rich stars, and this is just the beginning.
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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 | 0.001 | 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".