AN INFRARED CENSUS OF DUST IN NEARBY GALAXIES WITH<i>SPITZER</i>(DUSTiNGS). II. DISCOVERY OF METAL-POOR DUSTY AGB STARS
Bibliographic record
Abstract
The DUSTiNGS survey (DUST in Nearby Galaxies with Spitzer ) is a 3.6 and 4.5 μm imaging survey of 50 nearby dwarf galaxies designed to identify dust-producing asymptotic giant branch (AGB) stars and massive stars. Using two epochs, spaced approximately six months apart, we identify a total of 526 dusty variable AGB stars (sometimes called "extreme" or x-AGB stars; [3.6]–[4.5] > 0.1 mag). Of these, 111 are in galaxies with [Fe/H] < −1.5 and 12 are in galaxies with [Fe/H] < −2.0, making them the most metal-poor dust-producing AGB stars known. We compare these identifications to those in the literature and find that most are newly discovered large-amplitude variables, with the exception of ≈30 stars in NGC 185 and NGC 147, 1 star in IC 1613, and 1 star in Phoenix. The chemical abundances of the x-AGB variables are unknown, but the low metallicities suggest that they are more likely to be carbon-rich than oxygen-rich and comparisons with existing optical and near-IR photometry confirm that 70 of the x-AGB variables are confirmed or likely carbon stars. We see an increase in the pulsation amplitude with increased dust production, supporting previous studies suggesting that dust production and pulsation are linked. We find no strong evidence linking dust production with metallicity, indicating that dust can form in very metal-poor environments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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 | 0.001 |
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".