The Pristine survey XI: the FORS2 sample
Bibliographic record
Abstract
Abstract Extremely metal-poor (EMP) stars are old objects that mostly formed very early after the big bang. They are rare and, to select them, we have to rely on low-resolution spectroscopic or photometric surveys; specifically the combination of narrow- and broad-band photometry provides a powerful and time efficient way to select MP stars. The Pristine photometric survey is using the Canada–France–Hawaii Telescope MegaCam wide-field imager to obtain narrow-band photometry by utilizing a filter centred at 395.2 nm on the Ca ii-H and -K lines. Gaia DR 2 is providing us the wide-band photometry as well as parallaxes. Follow-up observations of MP candidates allowed us to improve our photometric calibrations. In this paper of the series we analyse MP stars observed with FORS2 at VLT. We demonstrate the Pristine calibration adopted in this work to be able to provide metallicities accurate to ±0.3 dex for MP giant stars with good parallaxes, while it performs poorly for dwarf and turn-off stars, whatever the accuracy on the parallaxes. We find some MP and very MP stars that are not enhanced in α elements. Such stars have already been found in several other searches, and a higher resolution follow-up of our sample would be useful to put our findings on a firmer ground. This sample of stars analysed has a low fraction of carbon-enhanced MP stars, regardless of the definition adopted. This deficiency could indicate a small sensitivity of the Pristine filter to carbon abundance, issue to be addressed in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".