The Spatial Distribution of the Galactic First Stars. I. High‐Resolution<i>N</i>‐Body Approach
Bibliographic record
Abstract
We study the spatial distribution of Galactic metal-free stars by combining an extremely high-resolution (7.8 × 10 5 M ☉ per particle) cold dark matter N -body simulation of the Milky Way with a semianalytic model of metal enrichment. This approach allows us to resolve halos with virial temperatures down to the 10 4 K atomic cooling limit, and it is sufficiently flexible to make a number of robust conclusions, despite the extremely uncertain properties of the first stars. Galactic metal-free stars are formed over a large redshift range, which peaks at z ≈ 10, but continues down to z ≈ 5, contributing stars at a wide range of Galactocentric radii. Stars containing only metals from primordial stars are similarly widespread. Neither changing the efficiency of metal dispersal by 2 orders of magnitude, nor drastically changing the approximations in our semianalytical model can affect these result. Thus, if they have sufficiently long lifetimes, a significant number of stars formed in initially primordial star clusters should be found in the nearby Galactic halo. Observations of metal abundances in Galactic halo stars should be taken as directly constraining the properties of primordial stars, and the lack of metal-free halo stars today should be taken as strongly suggesting a 0.8 M ☉ lower limit on the primordial initial mass function.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".