The dust condensation sequence in red supergiant stars
Bibliographic record
Abstract
<i>Context. <i/>Red supergiant (RSG) stars exhibit significant mass loss by means of a slow, dense wind. They are often considered to be the more massive counterparts of Asymptotic Giant Branch (AGB) stars. While AGB mass loss is related to their strong pulsations, the RSG are often only weakly variable. This raises the question of whether their wind-driving mechanism and the dust composition of the wind are the same.<i>Aims. <i/>We study the conditions at the base of the wind by determining the dust composition of a sample of RSG. The dust composition is assumed to be sensitive to the density, temperature, and acceleration at the base of the wind. We compare the derived dust composition with the composition measured in AGB star winds.<i>Methods. <i/>We compile a sample of 27 RSG infrared spectra (ISO-SWS) and supplement these with photometric measurements to derive the full spectral energy distribution (SED). These data are modelled using a dust radiative-transfer code, taking into account the optical properties of the relevant candidate materials to search for correlations between mass-loss rate, density at the inner edge of the dust shell, and stellar parameters.<i>Results. <i/>We find strong correlations between the dust composition, mass-loss rate, and the stellar luminosity, roughly in agreement with the theoretical dust condensation sequence. We identify the need for a continuous (near-)IR dust opacity and tentatively propose amorphous carbon, and we note significant differences with AGB star winds in terms of the presence of PAHs, absence of “the” 13 <i>μ<i/>m band, and a lack of strong water bands.<i>Conclusions. <i/>Dust condensation in RSG is found to experience a freeze-out process that is similar to that in AGB stars. Together with the positive effect of the stellar luminosity on the mass-loss rate, this suggests that radiation pressure on dust grains is an important ingredient in the driving mechanism. Still, differences with AGB stars are manifold and thus the winds of RSG should be studied individually in further detail.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".