Chronicling the Reionization History at 6 ≲ z ≲ 7 with Emergent Quasar Damping Wings
Bibliographic record
Abstract
Abstract The spectra of high-redshift (z ≳ 6) quasars contain valuable information on the progression of the Epoch of Reionization. At redshifts z < 6, the observed Lyman-series forest shows that the intergalactic medium is nearly ionized, while at z > 7 the observed quasar damping wings indicate high neutral gas fractions. However, there remains a gap in neutral gas fraction constraints at 6 ≲ z ≲ 7 where the Lyman-series forest becomes saturated but damping wings have yet to fully emerge. In this work, we use a sample of 18 quasar spectra at redshifts 6.0 < z < 7.1 to close this gap. We apply neural networks to reconstruct the quasars’ continuum emission around the partially absorbed Lyα line to normalize their spectra, and stack these continuum-normalized spectra in three redshift bins. To increase the robustness of our results, we compare the stacks to a grid of models from two hydrodynamical simulations, ATON and CROC, and we measure the volume-averaged neutral gas fraction, x ¯ HI , while jointly fitting for the mean quasar lifetime, t Q, for each stacked spectrum. We chronicle the evolution of neutral gas fraction using the ATON (CROC) models as follows: x ¯ HI = 0.21 − 0.07 + 0.17 ( x ¯ HI = 0.10 < 10 − 4 + 0.73 ) at 〈z〉 = 6.10, x ¯ HI = 0.21 − 0.07 + 0.33 ( x ¯ HI = 0.57 − 0.47 + 0.26 ) at 〈z〉 = 6.46, and x ¯ HI = 0.37 − 0.17 + 0.17 (
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".