The diverse star formation histories of early massive, quenched galaxies in modern galaxy formation simulations
Bibliographic record
Abstract
ABSTRACT We present a comprehensive study of the star formation histories of massive-quenched galaxies at $z=3$ in three semi-analytical models (Shark, gaea, Galform) and three cosmological hydrodynamical simulations (Eagle, IllustrisTNG, Simba). We study the predicted number density and stellar mass function of massive-quenched galaxies, their formation and quenching time-scales and star formation properties of their progenitors. Predictions are disparate in all these diagnostics, for instance: (i) some simulations reproduce the observed number density of very massive-quenched galaxies ($\gt 10^{11}\, \rm {\rm M}_{\odot }$) but underpredict the high density of intermediate-mass ones, while others fit well the lower masses but underpredict the higher ones; (ii) in most simulations, except for gaea and Eagle, most massive-quenched galaxies had starburst periods, with the most intense ones happening at $4\lt z\lt 5$; however, only in Shark and IllustrisTNG we do find a large number of progenitors with star formation rates $\gt 300\rm \, {\rm M}_{\odot }\, yr^{-1}$; (iii) quenching time-scales are in the range $\approx 20\!-\!150$ Myr depending on the simulation; among other differences. These disparate predictions can be tied to the adopted active galactic nucleus (AGN) feedback model. For instance, the explicit black hole (BH) mass dependence to trigger the ‘radio mode’ in IllustrisTNG and Simba makes it difficult to produce quenched galaxies with intermediate stellar masses, also leading to higher baryon collapse efficiencies ($\approx 15\!-\!30$ per cent); while the strong bolometric luminosity dependence of the AGN outflow rate in gaea leads to BHs of modest mass quenching galaxies. Current observations are unable to distinguish between these different predictions due to the small sample sizes. However, these predictions are testable with current facilities and upcoming observations, allowing a ‘true physics experiment’ to be carried out.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".