Identification of a transition from stochastic to secular star formation around
Bibliographic record
Abstract
Star formation histories (SFHs) of early galaxies (6 < z < 12) have been found to be highly stochastic in both simulations and observations, while at z≲6 the presence of a main sequence (MS) of star-forming galaxies implies secular processes at play. In this work we characterise the SFH variability of early galaxies as a function of their stellar mass and redshift. We used the JADES public catalogue and derived the physical properties of the galaxies as well as their SFHs using the spectral energy distribution modelling code CIGALE. To this end, we implemented a non-parametric SFH with a flat prior allowing for as much stochasticity as possible. We used the star formation rate (SFR) gradient, an indicator of the movement of galaxies on the SFR–M* plane, linked to the recent SFH of galaxies. This dynamical approach of the relation between the SFR and stellar mass allows us to show that, at z > 9, 87% of massive galaxies (log(M*/M⊙)≳9) have SFR gradients consistent with a stochastic star formation activity during the last 100 Myr, while this fraction drops to 15% at z < 7. On the other hand, we see an increasing fraction of galaxies with a star formation activity following a common stream on the SFR–M* plane with cosmic time, indicating that a secular mode of star formation is emerging. We place our results in the context of the observed excess of UV emission as probed by the UV luminosity function at z ≳ 10 by estimating σUV, the dispersion of the UV absolute magnitude distribution, to be of the order of 1.2 mag, and compare it with predictions from the literature. In conclusion, we find a transition of star formation mode happening around z ∼ 9: Galaxies with stochastic SFHs dominate at z ≳ 9, although this level of stochasticity is too low to reach those invoked by recent models to reproduce the observed UV luminosity function.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".