Updated dark pixel fraction constraints on reionization’s end from the Lyman-series forests of XQR−30
Bibliographic record
Abstract
ABSTRACT The fraction of ‘dark pixels’ in the Ly$\alpha$ and other Lyman-series forests at $z\sim 5$–6 provides a powerful constraint on the end of the reionization process. Any spectral region showing transmission must be highly ionized, while dark regions could be ionized or neutral, thus the dark pixel fraction provides a (nearly) model independent upper limit to the volume-filling fraction of the neutral intergalactic medium, modulo choices in binning scale and dark pixel definition. Here, we provide updated measurements of the 3.3 comoving Mpc dark pixel fraction at $z=4.85$–6.25 in the Ly$\alpha$, Ly$\beta$, and Ly$\gamma$ forests of 34 deep $5.8 \lesssim z\lesssim 6.6$ quasar spectra from the (enlarged) XQR−30 sample. Using the negative pixel method to measure the dark pixel fraction, we derive fiducial $1\sigma$ upper limits on the volume-average neutral hydrogen fraction of $\langle x_{\rm HI} \rangle \le \lbrace 0.030+0.048,0.095+0.037,0.191+0.056,0.199+0.087\rbrace$ at $\bar{z}=\lbrace 5.481,5.654,5.831,6.043\rbrace$ from the optimally sensitive combination of the Ly$\beta$ and Ly$\gamma$ forests. We further demonstrate an alternative method that treats the forest flux as a mixture of dark and transparent regions, where the latter are modelled using a physically motivated parametric form for the intrinsic opacity distribution. The resulting model-dependent upper limits on $\langle x_{\rm HI} \rangle$ are similar to those derived from our fiducial model-independent analysis. We confirm that the bulk of reionization must be finished at $z>6$, while leaving room for an extended ‘soft landing’ to the reionization history down to $z\sim 5.4$ suggested by Ly$\alpha$ forest opacity fluctuations.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".