Reionization and galaxy inference from the high-redshift Ly α forest
Bibliographic record
Abstract
ABSTRACT The transmission of Lyman α (Ly α) in the spectra of distant quasars depends on the density, temperature, and ionization state of the intergalactic medium. Therefore, high-redshift (z > 5) Ly α forests could be invaluable in studying the late stages of the epoch of reionization (EoR), as well as properties of the sources that drive it. Indeed, high-quality quasar spectra have now firmly established the existence of large-scale opacity fluctuations at z > 5, whose physical origins are still debated. Here, we introduce a Bayesian framework capable of constraining the EoR and galaxy properties by forward-modelling the high-z Ly α forest. Using priors from galaxy and cosmic microwave background observations, we demonstrate that the final overlap stages of the EoR (when >95 per cent of the volume was ionized) should occur at z < 5.6, in order to reproduce the large-scale opacity fluctuations seen in forest spectra. However, it is the combination of patchy reionization and the inhomogeneous ultraviolet background that produces the longest Gunn–Peterson troughs. Ly α forest observations tighten existing constraints on the characteristic ionizing escape fraction of galaxies, with the combined observations suggesting $f_{\rm esc} \approx 7^{+4}_{-3}$ per cent, and disfavouring a strong evolution with the galaxy’s halo (or stellar) mass.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".