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 (850 μ m) spectral energy distributions (SEDs) of the 61 galaxies from the Key Insights on Nearby Galaxies: A Far-Infrared Survey with Herschel (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 ( M star ), star-formation rate (SFR), dust mass ( M dust ), 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 5–10 μ m region dominated by PAH emission, and also between 25 and 70 μ m 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 overestimates M star relative to the SED fitting, and we provide new SED-based formulations for estimating M star from WISE W1 (3.4 μ m) luminosities and colors. From a principal component analysis of M star , SFR, M dust , and O/H, we reproduce previous scaling relations among M star , SFR, and O/H, and find that M dust can be predicted to within ∼0.3 dex using only M star and SFR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".