IONIZING RADIATION FROM<i>z</i>= 4–10 GALAXIES
Bibliographic record
Abstract
We compute the escape of ionizing radiation from galaxies in the redshift interval z = 4–10, i.e., during and after the epoch of reionization, using a high-resolution set of galaxies, formed in fully cosmological simulations. The simulations invoke early, energetic feedback, and the galaxies evolve into a realistic population at z = 0. Our galaxies cover nearly four orders of magnitude in masses (10 7.8 –10 11.5 M ☉ ) and more than five orders in star formation rates (10 −3.5 –10 1.7 M ☉ yr −1 ), and we include an approximate treatment of dust absorption. We show that the source-averaged Lyman limit escape fraction at z = 10.4 is close to 80% declining monotonically with time as more massive objects build up at lower redshifts. Although the amount of dust absorption is uncertain to 1–1.5 dex, it is tightly correlated with metallicity; we find that dust is unlikely to significantly impact the observed UV output. These results support reionization by stellar radiation from low-luminosity dwarf galaxies and are also compatible with Lyman continuum observations and theoretical predictions at z ∼ 3–4.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".