Exploring the origin of low-metallicity stars in Milky-Way-like galaxies with the NIHAO-UHD simulations
Bibliographic record
Abstract
ABSTRACT The kinematics of the most metal-poor stars provide a window into the early formation and accretion history of the Milky Way (MW). Here, we use five high-resolution cosmological zoom-in simulations (∼ 5 × 106 star particles) of MW-like galaxies taken from the NIHAO-UHD project, to investigate the origin of low-metallicity stars ([Fe/H] ≤ −2.5). The simulations show a prominent population of low-metallicity stars confined to the disc plane, as recently discovered in the MW. The ubiquity of this finding suggests that the MW is not unique in this respect. Independently of the accretion history, we find that ≳90 per cent of the retrograde stars in this population are brought in during the initial build-up of the galaxies during the first few Gyr after the Big Bang. Our results therefore highlight the great potential of the retrograde population as a tracer of the early build-up of the MW. The prograde planar population, on the other hand, is accreted during the later assembly phase and samples the full galactic accretion history. In case of a quiet accretion history, this prograde population is mainly brought in during the first half of cosmic evolution (t ≲ 7 Gyr), while, in the case of an ongoing active accretion history, later mergers on prograde orbits are also able to contribute to this population. Finally, we note that the MW shows a rather large population of eccentric, very metal-poor planar stars. This is a feature not seen in most of our simulations, with the exception of one simulation with an exceptionally active early building phase.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".