The <scp>thesan-zoom</scp> project: burst, quench, repeat – unveiling the evolution of high-redshift galaxies along the star-forming main sequence
Bibliographic record
Abstract
ABSTRACT Characterizing the evolution of the star-forming main sequence (SFMS) at high redshift is crucial to contextualize the observed extreme properties of galaxies in the early Universe. We present an analysis of the SFMS and its scatter in the thesan-zoom simulations, where we find a redshift evolution of the SFMS normalization scaling as $\propto (1+z)^{2.64\pm 0.03}$, significantly stronger than is typically inferred from observations. We can reproduce the flatter observed evolution by filtering out weakly star-forming galaxies, implying that current observational fits are biased due to a missing population of lulling galaxies or overestimated star formation rates. We also explore star formation variability using the scatter of galaxies around the SFMS ($\sigma _{\mathrm{MS}}$). At the population level, the scatter around the SFMS increases with cosmic time, driven by the increased importance of long-term environmental effects in regulating star formation at later times. To study short-term star formation variability, or ‘burstiness’, we isolate the scatter on time-scales shorter than 50 Myr. The short-term scatter is larger at higher redshift, indicating that star formation is indeed more bursty in the early Universe. We identify two starburst modes: (i) externally driven, where rapid large-scale inflows trigger and fuel prolonged, extreme star formation episodes, and (ii) internally driven, where cyclical ejection and re-accretion of the interstellar medium in low-mass galaxies drive bursts, even under relatively steady large-scale inflow. Both modes occur at all redshifts, but the increased burstiness of galaxies at higher redshift is due to the increasing prevalence of the more extreme external mode of star formation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".