Modeling the Pan‐Spectral Energy Distribution of Starburst Galaxies. IV. The Controlling Parameters of the Starburst SED
Bibliographic record
Abstract
We combine the stellar spectral synthesis code Starburst99, the nebular modeling code MAPPINGS III and a one-dimensional dynamical evolution model of H II regions around massive clusters of young stars to generate improved models of the spectral energy distribution (SED) of starburst galaxies. We introduce a compactness parameter, , which characterizes the specific intensity of the radiation field at ionization fronts in H II regions and which controls the shape of the far-infrared (IR) dust reemission, often referred to loosely as the dust "temperature." We also investigate the effect of metallicity on the overall SED and in particular, on the strength of the polycyclic aromatic hydrocarbon (PAH) features. We provide templates for the mean emission produced by the young compact H II regions, the older (10-100 Myr) stars and for the wavelength-dependent attenuation produced by a foreground screen of the dust used in our model. We demonstrate that these components may be combined to produce a excellent fit to the observed SEDs of star formation-dominated galaxies which are often used as templates (Arp 220 and NGC 6240). This fit extends from the Lyman limit to wavelengths of about 1 mm. The methods presented in both this paper and in the previous papers of this series allow the extraction of the physical parameters of the starburst region (star formation rates, star formation rate history, mean cluster mass, metallicity, dust attenuation, and pressure) from the analysis of the pan-spectral SED.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".