GAMA/DEVILS: cosmic star formation and AGN activity over 12.5 billion years
Bibliographic record
Abstract
ABSTRACT We use the Galaxy and Mass Assembly (GAMA) and the Deep Extragalactic Visible Legacy Survey (DEVILS) observational data sets to calculate the cosmic star formation rate (SFR) and active galactic nuclei (AGN) bolometric luminosity history (CSFH/CAGNH) over the last 12.5 billion years. SFRs and AGN bolometric luminosities were derived using the spectral energy distribution fitting code ProSpect, which includes an AGN prescription to self-consistently model the contribution from both AGN and stellar emission to the observed rest-frame ultra-violet to far-infrared photometry. We find that both the CSFH and CAGNH evolve similarly, rising in the early Universe up to a peak at look-back time ≈10 Gyr (z ≈ 2), before declining towards the present day. The key result of this work is that we find the ratio of CAGNH to CSFH has been flat ($\approx 10^{42.5}\, \mathrm{erg \, s^{-1}\, {\rm M}_{\odot }^{-1}\, yr}$) for 11 Gyr up to the present day, indicating that star formation and AGN activity have been coeval over this time period. We find that the stellar masses of the galaxies that contribute most to the CSFH and CAGNH are similar, implying a common cause, which is likely gas inflow. The depletion of the gas supply suppresses cosmic star formation and AGN activity equivalently to ensure that they have experienced similar declines over the last 10 Gyr. These results are an important milestone for reconciling the role of star formation and AGN activity in the life cycle of galaxies.
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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 | 0.001 |
| 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 | 0.000 | 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".