Rotation, Convective Core Overshooting, and Period Changes in Classical Cepheid Stellar Evolution Models
Bibliographic record
Abstract
Abstract Classical Cepheids are powerful probes of both stellar evolution and near-field cosmology thanks to their great luminosities, pulsations, and their adherence to the Leavitt (period–luminosity) Law. However, there still exist a number of questions regarding their evolution, such as the roles of rotation, convective core overshooting, and winds. ln particular, how do these processes impact Cepheid evolution and the predicted fundamental properties such as stellar mass? In this work, we compare a sample of Cepheids with measured rates of period change with new evolution models to test the impact of these first two processes. In our previous study we found that enhanced mass loss is crucial for describing the sample, and here we continue that analysis but for rotational mixing and core overshooting. We show that while rotation is important for stellar evolution studies, rotation itself is insufficient to model the distribution of period change rates from the observed sample. On the other hand, convective core overshooting is needed to explain the magnitude of the rates of period change, but does not explain the number of stars with positive and negative period change rates. In conclusion, we determine that convective core overshooting and stellar rotation alone are not enough to account for the observed distribution of Cepheid rates of period change and another mechanism, such as pulsation-driven mass loss, may be required.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".