TODDLERS: A new UV-millimeter emission library for star-forming regions
Bibliographic record
Abstract
Context. The current generation galaxy formation simulations often approximate star formation, making it necessary to use models of star-forming regions to produce observables from such simulations. In the first paper of this series, we introduced TODDLERS, a physically motivated, time-resolved model for UV–millimeter (mm) emission from star-forming regions, implemented within the radiative transfer code SKIRT. In this work, we use the SKIRT-TODDLERS pipeline to produce synthetic observations. Aims. We aim to demonstrate the potential of TODDLERS model through observables and quantities pertaining to star-formation. An additional goal is to compare the results obtained using TODDLERS with the existing star-forming regions model in SKIRT. Methods. We calculated broadband and line emission maps for the 30 Milky Way-like galaxies of the Auriga zoom simulation suite at a redshift of zero. Analyzing far-ultraviolet (FUV) and infrared (IR) broadband data, we calculated kiloparsec (kpc)-resolved IR correction factors, kIR, which allowed us to quantify the ratio of FUV luminosity absorbed by dust to reprocessed IR luminosity. Furthermore, we used the IR maps to calculate the kpc-scale mid-infrared (MIR) colors (8 μm/24 μm) and far-infrared (FIR) colors (70 μm/500 μm) of the Auriga galaxies. We used Hα and Hβ line maps to study the Balmer decrement and dust correction. We verified the fidelity of our model’s FIR fine structure lines as star formation rate (SFR) indicators. Results. The integrated UV-mm spectral energy distributions (SEDs) exhibit higher FUV and near-ultraviolet (NUV) attenuation and lower 24 μm emission compared to the existing star-forming regions model in SKIRT, alleviating tensions with observations reported in earlier studies. The light-weighted mean kIR increases with aperture and inclination, while its correlation with kpc-resolved specific star-formation rate (sSFR) is weaker than literature values from resolved SED fitting, potentially due to inaccuracies in local energy balance representation. The kpc-scale MIR-FIR colors show an excellent agreement with local observational data, with anti-correlation degree varying by galaxy morphology. We find that the Balmer decrement effectively corrects for dust, with the attenuation law varying with dust amount. The Hα emission attenuation levels in our models are comparable to those observed in the high-density regions of state-of-the-art radiation hydrodynamical simulations. The FIR fine-structure line emission-based luminosity-SFR relations are consistent with global observational relations, with the [C II] line displaying the best agreement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".