Ionizing properties of galaxies in JADES for a stellar mass complete sample: resolving the cosmic ionizing photon budget crisis at the Epoch of Reionization
Bibliographic record
Abstract
ABSTRACT We use NIRCam imaging from the JWST Advanced Deep Extragalactic Survey (JADES) to study the ionizing properties of a sample of 14 652 galaxies at $3 \le z_{\rm {phot}} \le 9$, 90 per cent complete in stellar mass down to log(M$_{\star }$/[M$_{\odot }])\approx 7.5$. Out of the full sample, 1620 of the galaxies have spectroscopic redshift measurements from the literature. We use the spectral energy distribution fitting code Prospector to fit all available photometry and infer galaxy properties. We find a significantly milder evolution of the ionizing photon production efficiency ($\xi _{\rm {ion}}$) with redshift and UV magnitude than previously reported. Interestingly, we observe two distinct populations in $\xi _{\rm {ion}}$, distinguished by their burstiness (given by SFR$_{10}$/SFR$_{100}$). Both populations show the same evolution with z and M$_{\rm {UV}}$, but have a different $\xi _{\rm {ion}}$ normalization. We convolve the more representative $\log (\xi _{\rm {ion}} (z,\text{M}_{\rm {UV}}))$ relations (accounting for $\sim 97$ % of the sample), with luminosity functions from literature, to place constraints on the cosmic ionizing photon budget. By combining our results, we find that one of our models can match the observational constraints from the Ly $\alpha$ forest at $z\lesssim 6$. We conclude that galaxies with M$_{\rm {UV}}$ between $-16$ and $-20$, adopting a reasonable escape fraction, can produce enough ionizing photons to ionize the Universe, without exceeding the required ionizing photon budget.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".