EU Del: exploring the onset of pulsation-driven winds in giant stars
Bibliographic record
Abstract
We explore the wind-driving mechanism of giant stars through the nearby (117 pc), intermediate-luminosity (L ≈ 1600 L⊙) star EU Del (HIP 101810, HD 196610). Atacama Pathfinder Experiment observations of the CO (3–2) and (2–1) transitions are used to derive a wind velocity of 9.51 ± 0.02 km s−1, a 12C/13C ratio of 14|$^{+9}_{-4}$| and a mass-loss rate of a few × 10−8 M⊙ yr−1. Analysis of published spectra show the star has a metallicity of [Fe/H] = −0.27 ± ∼0.30 dex. The star's dusty envelope lacks a clear 10-μm silicate feature, despite the star's oxygen-rich nature. Radiative transfer modelling cannot fit a wind acceleration model which relies solely on radiation pressure on condensing dust. We compare our results to VY Leo (HIP 53449), a star with similar temperature and luminosity, but different pulsation properties. We suggest the much stronger mass-loss from EU Del may be driven by long-period stellar pulsations, due to its potentially lower mass. We explore the implications for the mass-loss rate and wind velocities of other stars.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".