DustPedia: the relationships between stars, gas, and dust for galaxies residing in different environments
Bibliographic record
Abstract
We use a sub-set of the DustPedia galaxy sample (461 galaxies) to investigate the effect the environment has had on galaxies. We consider Virgo cluster and field samples and also assign a density contrast parameter to each galaxy, as defined by the local density of SDSS galaxies. We consider their chemical evolution (using MDust/MBaryon and MGas/MBaryon), their specific star formation rate (SFR/MStars), star formation efficiency (SFR/MGas), stars-to-dust mass ratio (MStars/MDust), gas-to-dust mass ratio (MGas/MDust) and the relationship between star formation rate per unit mass of dust and dust temperature (SFR/MDust and TDust). Late type galaxies (later than Sc) in all of the environments can be modelled using simple closed box chemical evolution and a simple star formation history (SFR(t) ∝ t exp−t/τ). For earlier type galaxies the physical mechanisms that give rise to their properties are clearly much more varied and require a more complicated model (mergers, gas in or outflow). However, we find little or no difference in the properties of galaxies of the same morphological type within the cluster, field or with different density contrasts. It appears that it is morphology, how and whenever this is laid down, and consistent internal physical processes that primarily determine the derived properties of galaxies in the DustPedia sample and not processes related to differences in the local environment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".