A multiwavelength analysis of the clumpy FIR-bright sources in M33
Bibliographic record
Abstract
We present a multiwavelength study of a sample of far-infrared (FIR) sources detected on the Herschel broad-band maps of the nearby galaxy M33. We perform source photometry on the FIR maps as well as mid-infrared (MIR), Hα, far-ultraviolet and integrated H i and CO line emission maps. By fitting MIR/FIR dust emission spectra, the source dust masses, temperatures and luminosities are inferred. The sources are classified based on their Hα morphology (sub-structured versus not-substructured) and on whether they have a significant CO detection (S/N > 3σ). We find that the sources have dust masses in the range 102–104 M⊙ and that they present significant differences in their inferred dust/star formation/gas parameters depending on their Hα morphology and CO detection classification. The results suggests differences in the evolutionary states or in the number of embedded H ii regions between the sub-samples. The source background-subtracted dust emission seems to be predominantly powered by local star formation, as indicated by a strong correlation between the dust luminosity and the dust-corrected Hα luminosity and the fact that the extrapolated young stellar luminosity is high enough to account for the observed dust emission. Finally, we do not find a strong correlation between the dust-corrected Hα luminosity and the dust mass of the sources, consistent with previous results on the breakdown of simple scaling relations at sub-kpc scales. However, the scatter in the relation is significantly reduced by correcting the Hα luminosity for the age of the young stellar populations in the star-forming regions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".