Dust contribution to the panchromatic galaxy emission
Bibliographic record
Abstract
ABSTRACT We have developed a pipeline called mentari to generate the far-ultraviolet to far-infrared spectral energy distribution (SED) of galaxies from the Dusty SAGEsemi-analytic galaxy formation model. Dusty SAGEincorporates dust-related processes directly on top of the basic ingredients of galaxy formation like gas infall, cooling, star formation, feedback, and mergers. We derive a physically motivated attenuation model from the computed dust properties in Dusty SAGE, so each galaxy has a self-consistent set of attenuation parameters based on the complicated dust physics that occurred across the galaxy’s assembly history. Then, we explore several dust emission templates to produce infrared spectra. Our results show that a physically-motivated attenuation model is better for obtaining a consistent multiwavelength description of galaxy formation and evolution, compared to using a constant attenuation. We compare our predictions with a compilation of observations and find that the fiducial model is in reasonable agreement with: (i) the observed z= 0 luminosity functions from the far-ultraviolet to far-infrared simultaneously, and hence (ii) the local cosmic SED in the same range, (iii) the rest-frame K-band luminosity function across 0 < z< 3, and (iv) the rest-frame far-ultraviolet luminosity function across 0 < z< 1. Our model underproduces the far-ultraviolet emission at z= 2 and z= 3, which can be improved by altering the AGN feedback and dust processes in Dusty SAGE, However, this combination thus worsens the agreement at z= 0, which suggests that more detailed treatment of such processes is required.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".