<i>Gaia</i>’s brightest very metal-poor (VMP) stars
Bibliographic record
Abstract
Context. Gaia DR3 has offered the scientific community a remarkable dataset of approximately one million spectra acquired with the radial velocity spectrometer (RVS) in the calcium II triplet region, which is well suited to identify very metal-poor (VMP) stars. However, over 40% of these spectra have no released parameters by Gaia’s GSP-Spec pipeline in the domain of VMP stars, whereas VMP stars are key tracers of early Galactic evolution. Aims. We aim to provide spectroscopic metallicities for VMP stars using Gaia RVS spectra, thereby producing a catalogue of bright VMP stars distributed over the full sky that can serve as the basis for studies of early chemical evolution throughout the Galaxy. Methods. We selected VMP stars using photometric metallicities from the literature and analysed the Gaia RVS spectra to infer spectroscopic metallicities for these stars. Results. The inferred metallicities agree very well with literature high-resolution metallicities, with a median systematic offset of 0.1 dex and standard deviation of ∼0.15 dex. The purity of this sample in the VMP regime is ∼80%, with outliers representing a mere ∼3%. Conclusions. We have built an all-sky catalogue of ∼1500 stars available, featuring reliable spectroscopic metallicities down to [Fe/H] ∼ −4.0, of which ∼1000 are VMP stars. More than 75% of these stars have either no spectroscopic metallicity value in the literature to date or have been flagged as unreliable in their literature spectroscopic metallicity estimates. This catalogue of bright (G < 13) VMP stars is three times larger than the current sample of well-studied VMP stars in the literature in this magnitude range, making it ideal for high-resolution spectroscopic follow-ups and studies of the properties of VMP stars in different parts of our Galaxy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.007 |
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".