Bursting at the seams: the star-forming main sequence and its scatter at <i>z</i> = 3–9 using NIRCam photometry from JADES
Bibliographic record
Abstract
ABSTRACT We present a comprehensive study of the star-forming main sequence (SFMS) and its scatter at redshifts $3 \le z \le 9$, using Near Infrared Camera (NIRCam) photometry from the JADES (JWST Advanced Deep Extragalactic Survey) survey in the Great Observatories Origins Deep Survey (GOODS) South (GOODS-S) and North (GOODS-N) fields. Our analysis is based on a sample of galaxies that is stellar mass complete down to $\log \left(M_{\star }/{\rm M}_{\odot }\right) \approx 8.1$. The redshift evolution of the SFMS at an averaging time-scale of 10 Myr follows a relation, quantified by the specific star formation rates (sSFR$_{10}$), of $\mathrm{sSFR}\propto (1+z)^{\mu }$ with $\mu = 2.30^{+0.03}_{-0.01}$, in good agreement with theoretical predictions and the specific mass accretion rate of dark matter haloes. We find that the SFMS normalization varies in a complex way with the SFR averaging time-scale, reflecting the combined effects of bursty star formation and rising star formation histories (SFHs). We quantify the scatter of the SFMS, revealing that it decreases with longer SFR averaging time-scales, from $\sigma _{\rm {int}} \approx 0.4-0.5~\mathrm{dex}$ at 10 Myr to $\sigma _{\rm {int}} \approx 0.2~\mathrm{dex}$ at 100 Myr, indicating that shorter term fluctuations dominate the scatter, although long-term variations in star formation activity are also present. Our findings suggest that bursty SFHs are more pronounced at lower stellar masses. Furthermore, we explore the implications of our results for the observed overabundance of UV-bright galaxies at $z > 10$, concluding that additional mechanisms, such as top-heavy initial mass functions, increased star formation efficiencies, or increased burstiness in star formation are needed to explain these observations. Finally, we emphasize the importance of accurate stellar mass completeness limits when fitting the SFMS, especially for galaxies with bursty SFHs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".