The dustier early-type galaxies deviate from late-type galaxies’ scaling relations
Bibliographic record
Abstract
Several dedicated surveys focusing on early-type galaxies (ETGs) reveal that significant fractions of them are detectable in all interstellar medium phases studied to date. We select ETGs from the Herschel Reference Survey that have both far-infrared Herschel and either H i or CO detection (or both). We derive their star formation rates (SFRs), stellar masses and dust masses via modelling their spectral energy distributions. We combine these with literature information on their atomic and molecular gas properties, in order to relate their star formation, total gas mass and dust mass on global scales. The ETGs deviate from the dust mass–SFR relation and the Schmidt–Kennicutt relation that SDSS star-forming galaxies define: compared to SDSS galaxies, ETGs have more dust at the same SFR, or less SFR at the same dust mass. When placing them in the M⋆–SFR plane, ETGs show a much lower specific SFR as compared to normal star-forming galaxies. ETGs show a large scatter compared to the Schmidt–Kennicutt relation found locally within our Galaxy, extending to lower SFRs and gas mass surface densities. Using an ETG's SFR and the Schmidt–Kennicutt law to predict its gas mass leads to an underestimate. ETGs have similar observed-gas-to-modelled-dust mass ratios to star-forming galaxies of the same stellar mass, as well as they exhibit a similar scatter.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".