A fundamental problem in our understanding of low-mass galaxy evolution
Bibliographic record
Abstract
Recent studies have found a dramatic difference between the observed number density evolution of low-mass galaxies and that predicted by semi-analytic models. Whilst models accurately reproduce the z = 0 number density, they require that the evolution occurs rapidly at early times, which is incompatible with the strong late evolution found in observational results. We report here the same discrepancy in two state-of-the-art cosmological hydrodynamical simulations, which is evidence that the problem is fundamental. We search for the underlying cause of this problem using two complementary methods. First, we consider a narrow range in stellar mass of log (Mstar/(h−2 M⊙)) = 9–9.5 and look for evidence of a different history of today's low-mass galaxies in models and observations. We find that the exclusion of satellite galaxies from the analysis brings the median ages and star formation rates of galaxies into reasonable agreement. However, the models yield too few young, strongly star-forming galaxies. Secondly, we construct a toy model to link the observed evolution of specific star formation rates with the evolution of the galaxy stellar mass function. We infer from this model that a key problem in both semi-analytic and hydrodynamical models is the presence of a positive instead of a negative correlation between specific star formation rate and stellar mass. A similar positive correlation is found between the specific dark matter halo accretion rate and the halo mass, indicating that model galaxies are growing in a way that follows the growth of their host haloes too closely. It therefore appears necessary to find a mechanism that decouples the growth of low-mass galaxies, which occurs primarily at late times, from the growth of their host haloes, which occurs primarily at early times. We argue that the current form of star formation-driven feedback implemented in most galaxy formation models is unlikely to achieve this goal, owing to its fundamental dependence on host halo mass and time.
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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 | 0.000 |
| 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".