JADES: The production and escape of ionizing photons from faint Lyman-alpha emitters in the epoch of reionization
Bibliographic record
Abstract
We present the properties of 17 faint Lyman-α emitting galaxies (LAEs) at z > 5.8 from the JWST Advanced Deep Extragalactic Survey (JADES) spectroscopic data in the Hubble Ultra Deep Field/GOODS-S. These LAEs span a redshift range z ≈ 5.8 − 8.0 and a UV magnitude range MUV ≈ −17 to −20.6, with the Lyα equivalent width (EW) in the range ≈25 − 350 Å. The detection of other rest-optical emission lines in the spectra of these LAEs enables the determination of accurate systemic redshifts and Lyα velocity offsets, as well as the physical and chemical composition of their stars and interstellar media. These faint LAEs are consistent with metal-poor systems with high ionization parameters, similar to the general galaxy population at z > 6. We measured an average ionizing photon production efficiency, log(ξion/erg−1 Hz) ≈ 25.57 across our LAEs, which does not evolve strongly with redshift. We report an anti-correlation between the Lyα escape fraction and the velocity offset from systemic redshift, consistent with model expectations. We further find that the strength and velocity offset of Lyα are neither correlated with galaxy spectroscopic properties nor with ξion. We find a decrease in Lyα escape fractions with redshift, indicative of decreasing sizes of ionized bubbles around LAEs at high redshifts. We used a range of galaxy properties to predict Lyman continuum escape fractions for our LAEs, finding that the ionizing photon output into the intergalactic medium from our LAEs remains roughly constant across the observed Lyα EW, showing a mild increase at fainter UV magnitudes and at higher redshifts. We derived correlations between the ionizing photon output from LAEs and their UV magnitudes, Lyα strengths and redshifts, which can be used to constrain the ionizing photon contribution of LAEs at z > 6 towards cosmic reionization.
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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 | 0.001 |
| 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".