Comprehensive comparison of models for spectral energy distributions from 0.1<i>μ</i>m to 1 mm of nearby star-forming galaxies
Bibliographic record
Abstract
We have fit the far-ultraviolet (FUV) to sub-millimeter (850μm) spectral energy distributions (SEDs) of the 61 galaxies from the Key Insights on Nearby Galaxies: A Far-Infrared Survey withHerschel(KINGFISH). The fitting has been performed using three models: the Code for Investigating GALaxy Evolution (CIGALE), the GRAphite-SILicate approach (GRASIL), and the Multiwavelength Analysis of Galaxy PHYSical properties (MAGPHYS). We have analyzed the results of the three codes in terms of the SED shapes, and by comparing the derived quantities with simple “recipes” for stellar mass (Mstar), star-formation rate (SFR), dust mass (Mdust), and monochromatic luminosities. Although the algorithms rely on different assumptions for star-formation history, dust attenuation and dust reprocessing, they all well approximate the observed SEDs and are in generally good agreement for the associated quantities. However, the three codes show very different behavior in the mid-infrared regime: in the 5–10μm region dominated by PAH emission, and also between 25 and 70μm where there are no observational constraints for the KINGFISH sample. We find that different algorithms give discordant SFR estimates for galaxies with low specific SFR, and that the standard recipes for calculating FUV absorption overestimate the extinction compared to the SED-fitting results. Results also suggest that assuming a “standard” constant stellar mass-to-light ratio overestimatesMstarrelative to the SED fitting, and we provide new SED-based formulations for estimatingMstarfrom WISE W1 (3.4μm) luminosities and colors. From a principal component analysis ofMstar, SFR,Mdust, and O/H, we reproduce previous scaling relations amongMstar, SFR, and O/H, and find thatMdustcan be predicted to within ∼0.3 dex using onlyMstarand SFR.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".