Cosmic Star Formation History and Its Dependence on Galaxy Stellar Mass
Bibliographic record
Abstract
We examine the cosmic star formation rate (SFR) and its dependence on galaxy stellar mass over the redshift range 0.8 < z < 2 using data from the Gemini Deep Deep Survey (GDDS). The SFR in the most massive galaxies ( M * > 10 10.8 M ☉ ) was 6 times higher at z = 2 than it is today. It drops steeply from z = 2, reaching the present-day value at z ~ 1. In contrast, the SFR density of intermediate-mass galaxies (10 10.2 M ☉ ≤ M * < 10 10.8 M ☉ ) declines more slowly and may peak or plateau at z ~ 1.5. We use the characteristic growth time t SFR ≡ ρ /ρ SFR to provide evidence of an associated transition in massive galaxies from a burst to a quiescent star formation mode at z ~ 2. Intermediate-mass systems transit from burst to quiescent mode at z ~ 1, while the lowest mass objects undergo bursts throughout our redshift range. Our results show unambiguously that the formation era for galaxies was extended and proceeded from high- to low-mass systems. The most massive galaxies formed most of their stars in the first ~3 Gyr of cosmic history. Intermediate-mass objects continued to form their dominant stellar mass for an additional ~2 Gyr, while the lowest mass systems have been forming over the whole cosmic epoch spanned by the GDDS. This view of galaxy formation clearly supports "downsizing" in the SFR where the most massive galaxies form first and galaxy formation proceeds from larger to smaller mass scales.
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".