The <scp>thesan-zoom</scp> project: star formation efficiencies in high-redshift galaxies
Bibliographic record
Abstract
ABSTRACT Recent James Webb Space Telescope observations hint at unexpectedly intense cosmic star formation in the early Universe, often attributed to enhanced star formation efficiencies (SFEs). Here, we analyse the SFE in thesan-zoom, a novel zoom-in radiation-hydrodynamic simulation campaign of high-redshift ($z \gtrsim 3$) galaxies employing a state-of-the-art galaxy formation model resolving the multiphase interstellar medium (ISM). The halo-scale SFE ($\epsilon ^{\ast }_{\rm halo}$) – the fraction of baryons accreted by a halo that are converted to stars – follows a double power-law dependence on halo mass, with a mild redshift evolution above $M_{\rm halo} \gtrsim 10^{9.5}{\, \rm M_\odot }$. The power-law slope transitions from $\sim 2/3$ to $\sim 1/3$ as halo mass increases, which hints at a transition from energy-driven to momentum-driven outflow. $\epsilon ^{\ast }_{\rm halo}$ is a factor of $2\!-\!3$ larger than commonly assumed in empirical galaxy formation models at $M_{\rm halo} \lesssim 10^{11}{\, \rm M_\odot }$. On galactic (pkpc) scales, the Kennicutt–Schmidt relation of neutral gas is universal in thesan-zoom, following $\Sigma _{\rm SFR} \propto \Sigma _{\rm gas}^2$, indicative of a turbulent energy balance in the ISM maintained by stellar feedback. The rise of $\epsilon ^{\ast }_{\rm halo}$ with halo mass can be traced primarily to increasing gas surface densities in massive galaxies. These results are robust against variations in numerical resolution and star formation and feedback models, depending mainly on the total feedback momentum budget. Although the increase in $\epsilon ^{\ast }_{\rm halo}$ with redshift is modest, it is sufficient to explain the large observed number density of UV-bright galaxies at $z \gtrsim 12$. However, reproducing the brightest sources at $M_{\rm UV} \lesssim -21$ may require extrapolating the SFE beyond the halo mass range covered by thesan-zoom.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".