A classification of CEMP stars based on neutron density that reveals the important role of the i process and the need for better nuclear physics data
Bibliographic record
Abstract
Talk at Nuclear Physics in Astrophysics IX, Sep 15-20, 2019 - Castle Waldthausen - Frankfurt - Germany Abstract Most C-enhanced metal poor stars show simultaneously substantial enhancements of heavy n-capture elements, such as Sr, Y, Zr, Ba, La and Eu. These have been commonly classified according to the presence of the element Ba which is dominantly made by the s process and of Eu which has an s-process production contribution of only ∼3%, and is therefore considered an r-process element. We are revisiting the classification of CEMP stars with the goal to establish more granular criteria based on the neutron-density prevailing in the stellar nuclear production site. To this end we have constructed equilibrium nucleosynthesis simulations for 6 < log Nn < 23. These models are independent of any specific astrophysical site. We compare the simulations with the JINAbase data base of CEMP stars. A large proportion of stars labeled CEMP-s in that data base cannot be reproduced by s-process neutron density models, but instead agree better with elemental ratios characteristic for the intermediate neutron density. Our analysis involves elemental ratios of both first and second peak, and reveals that predictions of observational abundance ratios are severely limited by nuclear physics uncertainties, especially the (n, γ) rate of n-rich unstable species 2 - 6 masses off the valley of stability. This new type of analysis lends itself to a systematic nuclear physics impact study approach, and we will present results from three such studies that have revealed a handful of (n,γ) reactions that should be measured with the highest priority. We will briefly summarize the present state of simulations in 3D and 1D of the most likely astrophysical sites of the i process.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".