The <scp>thesan-zoom</scp> project: central starbursts and inside-out quenching govern galaxy sizes in the early Universe
Bibliographic record
Abstract
ABSTRACT We explore the evolution of galaxy sizes at high redshift ($3< z < 13$) using the high-resolution thesan-zoom radiation-hydrodynamics simulations, focusing on the mass range of $10^6\, \mathrm{M}_{\odot } < \mathit{M}_{\ast } < 10^{10}\, \mathrm{M}_{\odot }$. Our analysis reveals that galaxy size growth is tightly coupled to bursty star formation. Galaxies above the star-forming main sequence tend to form stars in a central starburst, which decreases their radial size. These galaxies quench inside-out, causing spatially extended star formation and increasing their radial size, leading to oscillatory behaviour around the size–mass relation. Notably, we find a positive intrinsic size–mass relation at high redshift, consistent with observations but in tension with large-volume simulations. We attribute this discrepancy to the bursty star formation captured by our multiphase interstellar medium framework, but missing from simulations using the effective equation-of-state approach with hydrodynamically decoupled feedback. We also find that the normalization of the size–mass relation follows a double power law as a function of redshift, with a break at $z\approx 6$, because the majority of galaxies at $z>6$ show rising star-formation histories, and therefore are in a compaction phase. We demonstrate that H $\alpha$ emission is systematically extended relative to the UV continuum by a median factor of 1.7, consistent with recent James Webb Space Telescope studies. However, in contrast to previous interpretations that link extended H $\alpha$ sizes to inside-out growth, we find that Lyman-continuum (LyC) emission is spatially disconnected from H $\alpha$. Instead, a simple Strömgren sphere argument reproduces observed trends, suggesting that extreme LyC production during central starbursts is the primary driver of extended nebular emission.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.010 | 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".