Models of Metal‐poor Stars with Gravitational Settling and Radiative Accelerations. I. Evolution and Abundance Anomalies
Bibliographic record
Abstract
Evolutionary models have been calculated for Population II stars of 0.5-1.0 M ☉ from the pre-main sequence to the lower part of the giant branch. Rosseland opacities and radiative accelerations were calculated taking into account the concentration variations of 28 chemical species, including all species contributing to Rosseland opacities in the OPAL tables. The effects of radiative accelerations, thermal diffusion, and gravitational settling are included. While models were calculated for both Z = 0.00017 and 0.0017, we concentrate on models with Z = 0.00017 in this paper. These are the first Population II models calculated taking radiative acceleration into account. It is shown that, at least in a 0.8 M ☉ star, it is a better approximation not to let Fe diffuse than to calculate its gravitational settling without including the effects of g rad (Fe). In the absence of any turbulence outside of convection zones, the effects of atomic diffusion are large mainly for stars more massive than 0.7 M ☉ . Overabundances are expected in some stars with T eff ≥ 6000 K. Most chemical species heavier than CNO are affected. At 12 Gyr, overabundance factors may reach 10 in some cases (e.g., for Al or Ni), while others are limited to 3 (e.g., for Fe). The calculated surface abundances are compared to recent observations of abundances in globular clusters as well as to observations of Li in halo stars. It is shown that, as in the case of Population I stars, additional turbulence appears to be present. Series of models with different assumptions about the strength of turbulence were then calculated. One series minimizes the spread on the Li plateau, while another was chosen with turbulence similar to that present in AmFm stars of Population I. Even when turbulence is adjusted to minimize the reduction of Li abundance, there remains a reduction by a factor of at least 1.6 from the original Li abundance. Independent of the degree of turbulence in the outer regions, gravitational settling of He in the central region reduces the lifetime of Population II stars by 4%-7% depending on the criterion used. The effect on the age of the oldest clusters is discussed in a forthcoming paper (Paper II). Just as in Population I stars where only a fraction of stars, such as AmFm stars, have abundance anomalies, one should look for the possibility of abundance anomalies of metals in some Population II turnoff stars but not necessarily in all. Expected abundance anomalies are calculated for 28 species and compared to observations of M92 as well as to Li observations in halo field stars.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".