AN INFRARED CENSUS OF DUST IN NEARBY GALAXIES WITH <i>SPITZER</i> (DUSTINGS). I. OVERVIEW
Bibliographic record
Abstract
Nearby resolved dwarf galaxies provide excellent opportunities for studying the dust-producing late stages of stellar evolution over a wide range of metallicity (−2.7 ≲ [Fe/H] ≲ −1.0). Here, we describe DUSTiNGS (DUST in Nearby Galaxies with Spitzer ): a 3.6 and 4.5 μm post-cryogen Spitzer Space Telescope imaging survey of 50 dwarf galaxies within 1.5 Mpc that is designed to identify dust-producing asymptotic giant branch (AGB) stars and massive stars. The survey includes 37 dwarf spheroidal, 8 dwarf irregular, and 5 transition-type galaxies. This near-complete sample allows for the building of statistics on these rare phases of stellar evolution over the full metallicity range. The photometry is >75% complete at the tip of the red giant branch for all targeted galaxies, with the exception of the crowded inner regions of IC 10, NGC 185, and NGC 147. This photometric depth ensures that the majority of the dust-producing stars, including the thermally pulsing AGB stars, are detected in each galaxy. The images map each galaxy to at least twice the half-light radius to ensure that the entire evolved star population is included and to facilitate the statistical subtraction of background and foreground contamination, which is severe at these wavelengths. In this overview, we describe the survey, the data products, and preliminary results. We show evidence for the presence of dust-producing AGB stars in eight of the targeted galaxies, with metallicities as low as [Fe/H] = −1.9, suggesting that dust production occurs even at low 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".