The structural properties of nearby dwarf galaxies in low-density environments – size, surface brightness, and colour gradients
Bibliographic record
Abstract
ABSTRACT We use a complete sample of 211 nearby ($z< 0.08$), dwarf (10$^{8}$ M$_{\odot }$ < $M_{\rm {\star }}$ < 10$^{9.5}$ M$_{\odot }$) galaxies in low-density environments, to study their structural properties: effective radii ($R_{\rm e }$), effective surface brightnesses ($\langle \mu \rangle _{\rm e}$), and colour gradients. We explore these properties as a function of stellar mass and the three principal dwarf morphological types identified in a companion paper – early-type galaxies (ETGs), late-type galaxies (LTGs), and featureless systems. The median $R_{\rm e }$ of LTGs and featureless galaxies are factors of $\sim$2 and $\sim$1.2 larger than the ETGs. While the median $\langle \mu \rangle _{\rm e}$ of the ETGs and LTGs is similar, the featureless class is $\sim$1 mag arcsec$^{-2}$ fainter. Although they have similar median $R_{\rm e }$, the featureless and ETG classes differ significantly in their median $\langle \mu \rangle _{\rm e}$, suggesting that their evolution is different and that the featureless galaxies are not a subset of the ETGs. While massive ETGs typically exhibit negative or flat colour gradients, dwarf ETGs generally show positive colour gradients (bluer centres). The growth of ETGs therefore changes from being ‘outside-in’ to ‘inside-out’ as we move from the dwarf to the massive regime. The colour gradients of dwarf and massive LTGs are, however, similar. Around 46 per cent of dwarf ETGs show prominent, visually identifiable blue cores which extend out to $\sim$1.5$R_{\rm e}$. Finally, compared to their non-interacting counterparts, interacting dwarfs are larger, bluer at all radii and exhibit similar median $\langle \mu \rangle _{\rm e}$, indicating that interactions typically enhance star formation across the entire galaxy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".