TRACING THE STAR-FORMATION-DENSITY RELATION TO<i>z</i>∼ 2
Bibliographic record
Abstract
Recent work has shown that the star formation (SF) density relation—in which galaxies with low SF rates are preferentially found in dense environments—is still in place at z ∼ 1, but the situation becomes less clear at higher redshifts. We use mass-selected samples drawn from the UKIDSS Ultra-Deep Survey to show that galaxies with quenched SF tend to reside in dense environments out to at least z ∼ 1.8. Over most of this redshift range we are able to demonstrate that this SF–density relation holds even at fixed stellar mass. The environmental quenching of SF appears to operate with similar efficiency on all galaxies regardless of stellar mass. Nevertheless, the environment plays a greater role in the buildup of the red sequence at lower masses, whereas other quenching processes dominate at higher masses. In addition to a statistical analysis of environmental densities, we investigate a cluster at z = 1.6, and show that the central region has an elevated fraction of quiescent objects relative to the field. Although the uncertainties are large, the environmental quenching efficiency in this cluster is consistent with that of galaxy groups and clusters at z ∼ 0. In this work we rely on photometric redshifts and describe some of the pitfalls that large redshift errors can present.
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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 | 0.000 |
| 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 | 0.001 |
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".