TESTING MASS LOSS IN LARGE MAGELLANIC CLOUD CEPHEIDS USING INFRARED AND OPTICAL OBSERVATIONS
Bibliographic record
Abstract
It has been claimed that period-luminosity (P–L) relations derived from infrared observations of Large Magellanic Cloud (LMC) Cepheids are less dependent on the metallicity of the Cepheids. In this work, infrared observations of LMC Cepheids from the SAGE survey are combined with OGLE II optical observations to model and predict mass-loss rates. The mass-loss rates are fit to the data and are predicted to range from about 10 −12 to 10 −7 M ☉ /yr; however, the rates depend on the assumed value of the dust-to-gas ratio. By comparing the relations derived from observations to the relations derived from predicted infrared stellar luminosities from the mass-loss model, it is shown that mass loss affects the structure and scatter of the infrared P–L relation. Mass loss produces shallower slopes of the infrared relations and a lower zero point. There is also evidence for nonlinearity in the predicted P–L relations, and it is argued that mass loss produces larger infrared excess at lower periods, which affects the slope and zero point, making the P–L relations more linear in the wavelength range of 3.6 to 5.8 μm. Because the dust-to-gas ratio is metallicity dependent and mass loss may have a metallicity dependence, infrared P–L relations have additional uncertainty due to metallicity.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".