JINGLE V: Dust properties of nearby galaxies derived from hierarchical Bayesian SED fitting
Bibliographic record
Abstract
Abstract We study the dust properties of 192 nearby galaxies from the JINGLE survey using photometric data in the 22-850$\mu$m range. We derive the total dust mass, temperature T and emissivity index β of the galaxies through the fitting of their spectral energy distribution (SED) using a single modified black-body model (SMBB). We apply a hierarchical Bayesian approach that reduces the known degeneracy between T and β. Applying the hierarchical approach, the strength of the T-β anti-correlation is reduced from a Pearson correlation coefficient R = −0.79 to R = −0.52. For the JINGLE galaxies we measure dust temperatures in the range 17 − 30 K and dust emissivity indices β in the range 0.6 − 2.2. We compare the SMBB model with the broken emissivity modified black-body (BMBB) and the two modified black-bodies (TMBB) models. The results derived with the SMBB and TMBB are in good agreement, thus applying the SMBB, which comes with fewer free parameters, does not penalize the measurement of the cold dust properties in the JINGLE sample. We investigate the relation between T and β and other global galaxy properties in the JINGLE and Herschel Reference Survey (HRS) sample. We find that β correlates with the stellar mass surface density (R = 0.62) and anti-correlates with the HI mass fraction (MHI/M*, R = −0.65), whereas the dust temperature correlates strongly with the SFR normalized by the dust mass (R = 0.73). These relations can be used to estimate T and β in galaxies with insufficient photometric data available to measure them directly through SED fitting.
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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 | 0.002 |
| 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 | 0.000 |
| 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".