THE RADIUS DISCREPANCY IN LOW-MASS STARS: SINGLE VERSUS BINARIES
Bibliographic record
Abstract
A long-standing issue in the theory of low-mass stars is the discrepancy between predicted and observed radii and effective temperatures. In spite of the increasing availability of very precise radius determinations from eclipsing binaries and interferometric measurements of radii of single stars, there is no unanimous consensus on the extent (or even the existence) of the discrepancy and on its connection with other stellar properties (e.g., metallicity, magnetic activity). We investigate the radius discrepancy phenomenon using the best data currently available (accuracy ≲ 5%). We have constructed a grid of stellar models covering the entire range of low-mass stars (0.1–1.25 M ☉ ) and various choices of the metallicity and mixing length parameter, α. We used an improved version of the Yale Rotational stellar Evolution Code, implementing surface boundary conditions based on the most up-to-date PHOENIX atmosphere models. Our models are in good agreement with others in the literature and improve and extend the low mass end of the Yale-Yonsei isochrones. Our calculations include rotation-related quantities, such as moments of inertia and convective turnover timescales, useful in studies of magnetic activity and rotational evolution of solar-like stars. Consistent with previous works, we find that both binaries and single stars have radii inflated by about 3% with respect to the theoretical models; among binaries, the components of short orbital period systems are found to be the most deviant. We conclude that both binaries and single stars are comparably affected by the radius discrepancy phenomenon.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".