Large Ly α opacity fluctuations and low CMB τ in models of late reionization with large islands of neutral hydrogen extending to <i>z</i> &lt; 5.5
Bibliographic record
Abstract
Abstract High-redshift quasi-stellar object (QSO) spectra show large spatial fluctuations in the Ly α opacity of the intergalactic medium on surprisingly large scales at $z$ ≳ 5.5. We present a radiative transfer simulation of cosmic reionization driven by galaxies that reproduces this large scatter and the rapid evolution of the Ly α opacity distribution at 5 < $z$ < 6. The simulation also reproduces the low Thomson scattering optical depth reported by the latest cosmic microwave background (CMB) measurement and is consistent with the observed short near-zones and strong red damping wings in the highest redshift QSOs. It also matches the rapid disappearance of observed Ly α emission by galaxies at $z$ ≳ 6. Reionization is complete at $z$ = 5.3 in our model, and 50 per cent of the volume of the Universe is ionized at $z$ = 7. Agreement with the Ly α forest data in such a late reionization model requires a rapid evolution of the ionizing emissivity of galaxies that peaks at $z$ ∼ 6.8. The late end of reionization results in a large scatter in the photoionization rate and the neutral hydrogen fraction at redshifts as low as $z$ ≲ 5.5 with large residual neutral ‘islands’ that can produce very long Gunn–Peterson troughs resembling those seen in the data.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".