Deep Extragalactic VIsible Legacy Survey (DEVILS): satellite quenching at intermediate redshift
Bibliographic record
Abstract
ABSTRACT Determining the processes by which galaxies transition from a star-forming to a quiescent state (quenching) is paramount to our understanding of galaxy evolution. One of the key mechanisms by which this takes place is via a galaxy’s interactions with a local, overdense environment (satellite or environmental quenching). In the very local Universe, we see these processes in action, and can also observe their effects via the distribution of satellite galaxy properties. However, extending similar analyses outside of the local Universe is problematic, largely due to the difficulties in robustly defining environments with small and/or incomplete spectroscopic samples. We use new environmental metrics from the high-completeness Deep Extragalactic VIsible Legacy Survey (DEVILS) to explore the properties of satellite galaxies at intermediate redshift (0.3$<$z$<$0.5) and compare directly to the Galaxy And Mass Assembly Survey (GAMA) at 0$<$z$<$0.2. Importantly, both the galaxy properties and environmental metrics in DEVILS and GAMA are derived in an identical manner, reducing any methodology biases. We find: (i) that satellite galaxies in DEVILS and GAMA show suppressed star formation in comparison to isolated systems at the same stellar mass, by $\sim$0.5 dex in log$_{10}$(SFR/M$_{\odot }$ yr$^{-1}$), (ii) that this suppression is strongest in higher mass dark matter haloes (up to $\sim$1 dex in log$_{10}$(SFR/M$_{\odot }$ yr$^{-1}$) in the most massive haloes), and (iii) that at fixed stellar and halo mass, this suppression increases with time – with satellite passive fractions increasing by $\sim$10–15 per cent over the last $\sim$5 Gyr. This is consistent with previous observations and numerical simulations.
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) | 0.001 | 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".