Kpc-scale properties of dust temperature in terms of dust mass and star formation activity
Bibliographic record
Abstract
ABSTRACT We investigate how dust temperature is affected by local environmental quantities, especially dust surface density (Σdust), dust-to-gas ratio (D/G), and interstellar radiation field. We compile multiwavelength observations in 46 nearby galaxies, uniformly processed with a common physical resolution of 2 kpc. A physical dust model is used to fit the infrared dust emission spectral energy distribution (SED) observed with WISE and Herschel. The star formation rate (SFR) is traced with GALEX ultraviolet data corrected by WISE infrared. We find that the dust temperature correlates well with the SFR surface density (ΣSFR), which traces the radiation from young stars. The dust temperature decreases with increasing D/G at fixed ΣSFR, as expected from stronger dust shielding at high D/G, when ΣSFR is higher than $\sim 2\times 10^{-3}~\rm M_\odot ~yr^{-1}~kpc^{-2}$. These measurements are in good agreement with the dust temperature predicted by our proposed analytical model. Below this range of ΣSFR, the observed dust temperature is higher than the model prediction and is only weakly dependent on D/G, possibly due to dust heating from an old stellar population or the variation of SFR within the past 1010 yr. Overall, the dust temperature as a function of ΣSFR and Σdust predicted by our analytical model is consistent with observations. We also notice that, at fixed gas surface density, ΣSFR tends to increase with D/G, i.e. we can modify the Kennicutt–Schmidt law empirically with a dependence on D/G to match observations better.
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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 | 0.000 | 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".