The <i>Pristine</i> Survey – VIII. The metallicity distribution function of the Milky Way halo down to the extremely metal-poor regime
Bibliographic record
Abstract
ABSTRACT The Pristine survey uses narrow-band photometry to derive precise metallicities down to the extremely metal-poor regime ($ \rm [Fe/H] \lt -3$), and currently consists of over 4 million FGK-type stars over a sky area of $\sim 2500\, \mathrm{deg}^2$. We focus our analysis on a subsample of ∼80 000 main-sequence turn-off stars with heliocentric distances between 6 and 20 kpc, which we take to be a representative sample of the inner halo. The resulting metallicity distribution function (MDF) has a peak at $ \rm [Fe/H] =-1.6$, and a slope of Δ(LogN)/$\Delta \rm [Fe/H] = 1.0 \pm 0.1$ in the metallicity range of $-3.4\; \lt\; \rm [Fe/H]\; \lt -2.5$. This agrees well with a simple closed-box chemical enrichment model in this range, but is shallower than previous spectroscopic MDFs presented in the literature, suggesting that there may be a larger proportion of metal-poor stars in the inner halo than previously reported. We identify the Monoceros/TriAnd/ACS/EBS/A13 structure in metallicity space in a low-latitude field in the anticentre direction, and also discuss the possibility that the inner halo is dominated by a single, large merger event, but cannot strongly support or refute this idea with the current data. Finally, based on the MDF of field stars, we estimate the number of expected metal-poor globular clusters in the Milky Way halo to be 5.4 for $ \rm [Fe/H]\; \lt\; -2.5$ and 1.5 for $ \rm [Fe/H]\; \lt\; -3$, suggesting that the lack of low-metallicity globular clusters in the Milky Way is not due simply to statistical undersampling.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".