<tt>TODDLERS</tt>: a new UV–mm emission library for star-forming regions – I. Integration with <tt>SKIRT</tt> and public release
Bibliographic record
Abstract
ABSTRACT We present and publicly release a new star-forming regions emission library TODDLERS (Time evolution of Observables including Dust Diagnostics and Line Emission from Regions containing young Stars) for the publicly available radiative transfer code SKIRT. The library generation involves the spherical evolution of a homogeneous gas cloud around a young stellar cluster that accounts for stellar feedback processes including stellar winds, supernovae, and radiation pressure, as well as the gravitational forces on the gas. The semi-analytical evolution model is coupled with the photoionization code Cloudy to calculate time-dependent UV–mm spectral energy distributions (SEDs) from star-forming regions of varying metallicity, star-formation efficiency, birth-cloud density, and mass. The calculated SEDs include the stellar, nebular, and dust continuum emission along with a wide range of emission lines originating from H ii, photodissociation, and molecular gas regimes tabulated at high resolution. The SEDs incorporated in SKIRT are generated by calculating a stellar-mass normalized luminosity, which assumes that each emission source is composed of a power-law population of star-forming clouds. When compared to the previous treatment of star-forming regions in SKIRT, TODDLERS shows a better agreement with low-redshift observational data in the IR wavelength range while offering a more comprehensive line-emission support. This paves the way for a variety of applications using simulated galaxies at low and high redshift.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.031 |
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".