The UV continuum slopes of early star-forming galaxies in JADES
Bibliographic record
Abstract
ABSTRACT The power-law slope of the rest-ultraviolet (UV) continuum (fλ ∝ λβ) is a key metric of early star-forming galaxies, providing one of our only windows into the stellar populations and physical conditions of z ≳ 10 galaxies. Expanding upon previous studies with limited sample sizes, we leverage deep imaging from the JWST Advanced Deep Extragalactic Survey (JADES) to investigate the UV slopes of 179 z ≳ 9 galaxies with apparent magnitudes of mF200W ≃ 26–31, which display a median UV slope of β = −2.4. We compare to a statistical sample of z ≃ 5–9 galaxies, finding a shift towards bluer rest-UV colours at all $M_{\rm UV}$. The most UV-luminous z ≳ 9 galaxies are significantly bluer than their lower redshift counterparts, representing a dearth of moderately red galaxies within the first 500 Myr. At yet earlier times, the z ≳ 11 galaxy population exhibits very blue UV slopes, implying very low impact from dust attenuation. We identify a robust sample of 44 galaxies with β ≲ −2.8, which have spectral energy distributions requiring models of density-bounded H ii regions and median ionizing photon escape fractions of 0.51 to reproduce. Their rest-optical colours imply that this sample has weaker emission lines (median mF356W − mF444W = 0.19 mag) than typical galaxies (median mF356W − mF444W = 0.39 mag), consistent with the inferred escape fractions. This sample consists of relatively low stellar masses (median $\log (M/{\rm M}_{\odot })=7.5\pm 0.2$), and specific star formation rates (sSFRs; median $=79 \, \rm Gyr^{-1}$) nearly twice that of our full galaxy sample (median sSFRs $=44 \, \rm Gyr^{-1}$), suggesting these objects are more common among systems experiencing a recent upturn in star formation. We demonstrate that the shutoff of star formation provides an alternative solution for modelling of extremely blue UV colours, making distinct predictions for the rest-optical emission of these galaxies. Future spectroscopy will be required to distinguish between these physical pictures.
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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 | 0.001 | 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".