Low-mass bursty galaxies in JADES efficiently produce ionizing photons and could represent the main drivers of reionization
Bibliographic record
Abstract
ABSTRACT We use deep imaging from the JWST Advanced Deep Extragalactic Survey (JADES) to study the evolution of the ionizing photon production efficiency, ξion. We estimate ξion for a sample of 677 galaxies at z ∼ 4–9 using NIRCam (Near-Infrared Camera) photometry. Specifically, combinations of the medium and wide bands F335M–F356W and F410M–F444W to constrain emission lines that trace ξion: Hα and [O iii]. Additionally, we use the spectral energy distribution fitting code prospector to fit all available photometry and infer galaxy properties. The flux measurements obtained via photometry are consistent with FRESCO (First Reionisation Epoch Spectroscopic Complete Survey) and NIRSpec-derived fluxes. Moreover, the emission-line-inferred measurements are consistent with the prospector estimates. We also confirm the observed ξion trend with redshift and MUV, and find: log ξion(z, MUV) = (0.05 ± 0.02)z + (0.11 ± 0.02)MUV + (27.33 ± 0.37). We use prospector to investigate correlations of ξion with other galaxy properties. We see a clear correlation between ξion and burstiness in the star formation history of galaxies, given by the ratio of recent to older star formation, where burstiness is more prevalent at lower stellar masses. We also convolve our ξion relations with luminosity functions from the literature, and constant escape fractions of 10 per cent and 20 per cent, to place constraints on the cosmic ionizing photon budget. By combining our results, we find that if our sample is representative of the faint low-mass galaxy population, galaxies with bursty star formation are efficient enough in producing ionizing photons and could be responsible for the reionization of the Universe.
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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 | 0.001 | 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".