Investigating episodic mass loss in evolved massive stars
Bibliographic record
Abstract
Mass loss during the red supergiant (RSG) phase plays a crucial role in the evolution of an intermediate-mass star; however, the underlying mechanism remains unknown. We aim to increase the sample of well-characterized RSGs at subsolar metallicity by deriving the physical properties of 127 RSGs in nine nearby southern galaxies. For each RSG, we provide spectral types and used MARCS atmospheric models to measure stellar properties from their optical spectra, such as the effective temperature, extinction, and radial velocity. By fitting the spectral energy distribution, we obtained the stellar luminosity and radius for 92 RSGs, finding that ~50% of them have log(L/L⊙) ≥ 5.0 and six RSGs have R ≳ 1400 R⊙. We also find a correlation between the stellar luminosity and mid-IR excess of 33 dusty variable sources. Three of these dusty RSGs have luminosities exceeding the revised Humphreys-Davidson limit. We then derived a metallicity-dependent J − Ks color versus temperature relation from synthetic photometry and two new empirical J − Ks color versus temperature relations calibrated on literature TiO and J-band temperatures. To scale our derived cool TiO temperatures to values that are in agreement with the evolutionary tracks, we derived two linear scaling relations calibrated on J-band and i-band temperatures. We find that the TiO temperatures are more discrepant as a function of the mass-loss rate, and discuss future prospects of the TiO bands as a mass-loss probe. Finally, we speculate that three hot dusty RSGs may have experienced a recent mass ejection (12% of the K-type sample) and classify them as candidate Levesque-Massey variables.
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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 | 0.001 | 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) | 0.000 | 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".