The Pristine Dwarf-Galaxy survey
Bibliographic record
Abstract
Aims. The Milky Way has a large population of dwarf galaxy satellites. Their properties are sensitive to both cosmology and the physical processes underlying galaxy formation, but these properties are still not properly characterised for the entire satellite population. Methods. We aim to provide the most accurate systemic dynamical and metallicity properties of the dwarf galaxy Boötes II (Boo II). Results. We use a new spectroscopic sample of 39 stars in the field of Boo II (heliocentric distance of ∼66 kpc) with data from the Fiber Large Array Multi Element Spectrograph (FLAMES) mounted on the Very Large Telescope (VLT). The target selection is based on a combination of broadband photometry, proper motions from Gaia, and the metallicity-sensitive narrow-band photometry from the Pristine survey that is ideal for removing obvious Milky Way contaminants. Conclusions. We found nine new members, including five also found by recent works in the literature, and the farthest member to date (5.7 half-light radii from Boo II centroid), extending the spectroscopic spatial coverage of this system. Our metallicity measurements based on the Calcium triplet lines leads to the detection of the two first Extremely Metal-poor stars ([Fe/H] < −3.0) in Boo II. Combining this new dataset with literature data refines Boo II’s velocity dispersion (5.6−1.1+1.8 km s−1), systemic velocity (−126.8−1.5+2.0 km s−1), and shows that it does not show any sign of a significant velocity gradient (d⟨v⟩/dχ = 0.6−0.4+0.6 km s−1 arcmin−1, or −0.5/1.9 km s−1 arcmin−1 as 3σ upper limits). We are thus able to confirm the kinematic and metallicity properties of the satellite as well as identify new members for future high-resolution analyses.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".