On the fast quenching of young low-mass galaxies up to z ∼ 0.6: new spotlight on the lead role of environment
Bibliographic record
Abstract
We investigate the connection between environment and the different quenching channels that galaxies are prone to follow in the rest-frame NUVrK colour diagram, as identified by Moutard et al. (2016b). Namely, the fast-quenching channel followed by young low-mass galaxies and the slow-quenching channel followed by old high-mass ones. We make use of the >22 deg2 covered the VIPERS Multi-Lambda Survey to select a galaxy sample complete down to stellar masses of |$M_* \gt 10^{9.4} \,\mathrm{M}_\odot$| up to |$z$| ∼ 0.65 (|$M_* \gt 10^{8.8} \,\mathrm{M}_\odot$| up to |$z$| ∼ 0.5) and including 33 500 (43 000) quiescent galaxies properly selected at 0.2 < |$z$| < 0.65, while being characterized by reliable photometric redshifts (σδ|$z$|/(1 + |$z$|) ⩽ 0.04) that we use to measure galaxy local densities. We find that (1) the quiescence of low-mass [|$M_* \le 10^{9.7} \,\mathrm{M}_\odot$|] galaxies requires a strong increase of the local density, which confirms the lead role played by environment in their fast quenching and, therefore, confirms that the low-mass upturn observed in the stellar mass function of quiescent galaxies is due to environmental quenching. We also observe that (2) the reservoir of low-mass star-forming galaxies located in very dense regions (prone to environmental quenching) has grown between |$z$| ∼ 0.6 and |$z$| ∼ 0.4, while the share of low-mass quiescent galaxies (expected to being environmentally quenched) may have simultaneously increased, which would plead for a rising importance of environmental quenching with cosmic time, compared to mass quenching. We finally discuss the composite picture of such environmental quenching of low-mass galaxies and, in particular, how this picture may be consistent with a delayed-then-rapid quenching scenario.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".