The Pristine survey – XXI. Exploring the metal-poor boundary with ESPaDoNS
Bibliographic record
Abstract
ABSTRACT In this paper, we study high-resolution spectra of 19 stars that have metallicity estimates below –3.5 using at least two metallicity-sensitive photometric indices based on Pristine photometry. The purpose is to understand what kind of stars populate this parameter space, together with extremely metal-poor stars. This because we plan to extensively use the Pristine photometry to provide extremely metal-poor targets to the WEAVE spectroscopic survey and wish to understand the nature of possible contaminants. We find that this extreme sample of stars is heavily contaminated by variable stars, in particular short period eclipsing binaries. We thus found, serendipitously, eight double-lined spectroscopic eclipsing binaries that could be followed-up in future studies to provide reliable masses and distances for these systems. We also found two stars that have metallicity below –3.0, one of which may belong to the Gaia-Sausage-Enceladus structure. The lesson to be learned from this investigation is that to select truly metal-poor stars one should be able to remove all photometrically variable stars, which requires complementary information beyond the Pristine photometry. We show how the Gaia photometry can be used to remove about 85 per cent of the photometrically variable stars. Our investigation also shows that there is a clear potential for Pristine photometry to find double-lined spectroscopic binaries among short period eclipsing binaries.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".