Cool dust heating and temperature mixing in nearby star-forming galaxies
Bibliographic record
Abstract
Physical conditions of the interstellar medium in galaxies are closely linked to the ambient radiation field and the heating of dust grains.In order to characterize dust properties in galaxies over a wide range of physical conditions, we present here the radial surface brightness profiles of the entire sample of 61 galaxies from Key Insights into Nearby Galaxies: Far-Infrared Survey with Herschel (KINGFISH).The main goal of our work is the characterization of the grain emissivities, dust temperatures, and interstellar radiation fields (ISRFs) responsible for heating the dust.We first fit the radial profiles with exponential functions in order to compare stellar and cool-dust disk scalelengths, as measured by 3.6 μm and 250 μm surface brightnesses.Our results show that the stellar and dust scalelengths are comparable, with a mean ratio of 1.04, although several galaxies show dust-to-stellar scalelength ratios of 1.5 or more.We then fit the far-infrared spectral energy distribution (SED) in each annular region with single-temperature modified blackbodies using both variable (MBBV) and fixed (MBBF) emissivity indices β, as well as with physically motivated dust models.The KINGFISH profiles are well suited to examining trends of dust temperature T dust and β because they span a factor of ∼200 in the ISRF intensity heating the bulk of the dust mass, U min .Results from fitting the profile SEDs suggest that, on average, T dust , dust optical depth τ dust , and U min decrease with radius.The emissivity index β also decreases with radius in some galaxies, but in others is increasing, or rising in the inner regions and falling in the outer ones.Despite the fixed grain emissivity (average β ∼ 2.1) of the physically-motivated models, they are well able to accommodate flat spectral slopes with β < ∼ 1.An analysis of the wavelength variations of dust emissivities in both the data and the models shows that flatter slopes (β < ∼ 1.5) are associated with cooler temperatures, contrary to what would be expected from the usual T dust -β degeneracy.This trend is related to variations in U min since β and U min are very closely linked over the entire range in U min sampled by the KINGFISH galaxies: low U min is associated with flat β < ∼ 1.Both these results strongly suggest that the low apparent β values (flat slopes) in MBBV fits are caused by temperature mixing along the line of sight, rather than by intrinsic variations in grain properties.Finally, a comparison of dust models and the data show a slight ∼10% excess at 500 μm for low metallicity (12 + log (O/H) < ∼ 8) and low far-infrared surface brightness (Σ 500 ).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".