Prospects for distinguishing galaxy evolution models with surveys at redshifts z ≳ 4
Bibliographic record
Abstract
ABSTRACT Many semi-empirical galaxy formation models have recently emerged to interpret high-z galaxy luminosity functions and make predictions for future galaxy surveys. A common approach assumes a ‘universal’ star formation efficiency, f*, independent of cosmic time but strongly dependent on the masses of dark matter haloes. Though this class of models has been very successful in matching observations over much of cosmic history, simple stellar feedback models do predict redshift evolution in f* and are commonly used in semi-analytic models. In this work, we calibrate a set of universal f* and feedback-regulated models to the same set of rest-ultraviolet z ≳ 4 observations and find that a rapid, ∼(1 + z)−3/2 decline in both the efficiency of dust production and duty cycle of star formation are needed to reconcile feedback-regulated models with current observations. By construction, these models remain nearly identical to universal f* models in rest-ultraviolet luminosity functions (UVLFs) and colours. As a result, the only way to distinguish these competing scenarios is either via (i) improved constraints on the clustering of galaxies – universal and feedback-regulated models differ in predictions for the galaxy bias by 0.1 ≲ Δ〈b〉 ≲ 0.3 over 4 ≲ z ≲ 10 – or (ii) independent constraints on the dust contents and/or duty cycle of star formation. This suggests that improved constraints on the ‘dustiness’ and ‘burstiness’ of high-z galaxies will not merely add clarity to a given model of star formation in high-z galaxies, but rather fundamentally determine our ability to identify the correct model in the first place.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".