The DEEP2 Galaxy Redshift Survey: the role of galaxy environment in the cosmic star formation history
Bibliographic record
Abstract
Using galaxy samples drawn from the Sloan Digital Sky Survey and the DEEP2 Galaxy Redshift Survey, we study the relationship between star formation and environment at z∼ 0.1 and 1. We estimate the total star formation rate (SFR) and specific star formation rate (sSFR) for each galaxy according to the measured [O ii]λ 3727 Å nebular line luminosity, corrected using empirical calibrations to match more robust SFR indicators. Echoing previous results, we find that in the local Universe star formation depends on environment such that galaxies in regions of higher overdensity, on average, have lower SFRs and longer star formation time-scales than their counterparts in lower density regions. At z∼ 1, we show that the relationship between sSFR and environment mirrors that found locally. However, we discover that the relationship between total SFR and overdensity at z∼ 1 is inverted relative to the local relation. This observed evolution in the SFR–density relation is driven, in part, by a population of bright, blue galaxies in dense environments at z∼ 1. This population, which lacks a counterpart at z∼ 0, is thought to evolve into members of the red sequence from z∼ 1 to ∼0. Finally, we conclude that environment does not play a dominant role in the cosmic star formation history at z < 1: the dependence of the mean galaxy SFR on local galaxy density at constant redshift is small compared to the decline in the global SFR space density over the last 7 Gyr.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".