The quenching of star formation in dwarf galaxies: new perspectives from deep-wide surveys
Bibliographic record
Abstract
ABSTRACT Dwarf galaxies dominate the galaxy number density, making them critical to our understanding of galaxy evolution. However, typical dwarfs are too faint to be visible outside the very local Universe in past surveys like the SDSS, which offer large footprints but are shallow. Dwarfs in such surveys have relatively high star formation rates, which boost their luminosity, making them detectable in shallow surveys, but also biased and potentially unrepresentative of dwarfs as a whole. Here, we use deep data to perform an unbiased statistical study of $\sim$7000 nearby ($z\lt 0.25$) dwarfs (10$^8$ M$_{\odot }$ < $M_{\star }$ < 10$^{9.5}$ M$_{\odot }$) in the COSMOS field which, at these redshifts, is a relatively low-density region. At $z\sim 0.05$, $\sim$40 per cent of dwarfs in low-density environments are red/quenched, falling to $\sim$30 per cent by $z\sim 0.25$. Red dwarfs reside closer to nodes, filaments and massive galaxies. Proximity to a massive galaxy appears to be more important in determining whether a dwarf is red, rather than simply its distance from nodes and filaments or the mean density of its local environment. Interestingly, around half of the red dwarfs reside outside the virial radii of massive galaxies and around a third of those also inhabit regions in the lower 50 per cent in density percentile (i.e. regions of very low ambient density). Around half of the red dwarf population is therefore quenched by mechanisms unrelated to environment, which are likely to be internal processes such as stellar and active galactic nucleus feedback.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".