ROTATION AND MACROTURBULENCE IN METAL-POOR FIELD RED GIANT AND RED HORIZONTAL BRANCH STARS
Bibliographic record
Abstract
We report the results for rotational velocities, V rot sin i , and macroturbulence dispersions, ζ RT , for 12 metal-poor field red giant branch (RGB) stars and 7 metal-poor field red horizontal branch (RHB) stars. The results are based on Fourier transform analyses of absorption line profiles from high-resolution ( R ≈ 120,000), high-S/N (≈215 per pixel; ≈345 per resolution element) spectra obtained with the Gecko spectrograph at the Canada–France–Hawaii Telescope (CFHT). The stars were selected from the authors' previous studies of 20 RHB and 116 RGB stars, based primarily on larger-than-average line-broadening values. We find that ζ RT values for the metal-poor RGB stars are very similar to those for metal-rich disk giants studied earlier by Gray and his collaborators. Six of the RGB stars have small rotational values, less than 2.0 km s −1 , while five show significant rotation/enhanced line broadening, over 3 km s −1 . We confirm the rapid rotation rate for RHB star HD 195636, found earlier by Preston. This star's rotation is comparable to that of the fastest known rotating blue horizontal branch (BHB) stars, when allowance is made for differences in radii and moments of inertia. The other six RHB stars have somewhat lower rotation but show a trend to higher values at higher temperatures (lower radii). Comparing our results with those for BHB stars from Kinman et al., we find that the fraction of rapidly rotating RHB stars is somewhat lower than is found among BHB stars. The number of rapidly rotating RHB stars is also smaller than we would have expected from the observed rotation of the RGB stars. We devise two empirical methods to translate our earlier line-broadening results into V rot sin i for all the RGB and RHB stars they studied. Binning the RGB stars by luminosity, we find that most metal-poor field RGB stars show no detectable sign, on average, of rotation, which is not surprising given the stars' large radii. However, the most luminous stars, with M V ⩽ −1.5, do show net rotation, with mean values of 2–4 km s −1 , depending on the algorithm employed, and also show signs of radial velocity jitter and mass loss. This "rotation" may in fact prove to be due to other line-broadening effects, such as shock waves or pulsation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".