The impact of third dredge-up on the mass loss of Mira variables
Bibliographic record
Abstract
Context. The details of the mass-loss process in the late stages of low- and intermediate-mass stellar evolution are not well understood, in particular its dependence on stellar parameters. Mira variables are highly suitable targets for studying this mass-loss process. Aims. Here, we follow up on our earlier finding that a near-to-mid-infrared (NIR-MIR) colour versus pulsation period diagram shows two sequences of Miras that can be distinguished by the third dredge-up (3DUP) indicator technetium in those stars. While IR colours are good indicators of the dust mass-loss rate (MLR) from Miras, no corresponding sequences have been found using the gas MLR. However, investigations of the gas MLR have been hampered by data limitations. We aim to alleviate these limitations with new observational data. Methods. We present new optical spectra of a well-selected sample of Miras. We searched these spectra for absorption lines of Tc and other 3DUP indicators, and combine our findings with gas MLRs and expansion velocities from the literature. Furthermore, we extend the analysis of the MIR emission to WISE data and compare the broadband spectral energy distributions (SEDs) of Miras with and without Tc. Results. We find no systematic difference in gas MLRs between Miras with and without Tc. However, the gas envelopes of Tc-poor Miras appear to have a higher terminal expansion velocity than those of Miras with Tc. Furthermore, our analysis of the IR photometry strongly corroborates the earlier finding that Tc-poor Miras have a higher MIR emission than Tc-rich ones, by as much as a factor of two. We model the IR colours with DARWIN and stationary wind models and conclude that Miras with and without Tc have different dust content or dust properties. Conclusions. We discuss several hypotheses and interpretations of the observations and conclude that the reduction of free oxygen by 3DUP of carbon and iron-depleted dust grains in Tc-rich stars are the most convincing explanations for our observations.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".