Binarity among CEMP-no stars: an indication of multiple formation pathways?
Bibliographic record
Abstract
Carbon-enhanced metal-poor (CEMP) stars comprise a high percentage of stars at the lowest metallicities. The stars in the CEMP-no subcategory do not show any s -process enhancement and therefore cannot easily be explained by transfer of carbon and s -process elements from a binary AGB companion. We have performed radial velocity monitoring of a sample of 22 CEMP-no stars to further study the role that binarity plays in this type of CEMP star. We find four new binary CEMP-no stars based on their radial velocity variations; this significantly enlarges the population of known binaries to a total of 11. One of the new stars found to be in a binary system is HE 0107–5240, which is one of the most iron-poor stars known. This supports the binary transfer model for the origin of the abundance pattern of this star. We find a difference in binary fraction in our sample that depends on the absolute carbon abundance, with a binary fraction of 47 +15 −14 % for stars with a higher absolute carbon abundance and 18 +14 −9 % for stars with a lower absolute carbon abundance. This might imply a relation between a high carbon abundance and the binarity of a metal-poor star. Although binarity does not equate to mass transfer, there is a possibility that a CEMP-no star in a binary system has been polluted, and care has to be taken in the interpretation of their abundance patterns. We furthermore demonstrate the potential of Gaia of discovering additional binary candidates.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".