Subaru High-z Exploration of Low-Luminosity Quasars (SHELLQs) – XV. Constraining the cosmic reionization at 5.5 &lt; <i>z</i> &lt; 7
Bibliographic record
Abstract
ABSTRACT Revealing the cosmic hydrogen reionization history is one of the main goals of the modern cosmology. z > 5 quasars (QSOs) have been used as back-lights to investigate the evolution of the intervening intergalactic medium (IGM) during the cosmic reionization since their first discovery. However, due to the small population of luminous QSOs (∼130 QSOs known to date), a tight constraint on the reionization history has not yet been placed. In this work, we aim to tighten the constraint using the 93 QSOs (5.5 < z < 7.1) recently discovered in the Subaru High-z Exploration of Low-Luminosity Quasars (SHELLQS) project. This is the largest QSO sample used to constrain the epoch of reionization. We measure the mean IGM Ly α transmission and the QSO near-zone size using the UV spectra of these QSOs. The mean IGM Ly α transmission rises above zero at z ≲ 6, indicating the end of the reionization. The near-zone sizes of the SHELLQs QSOs are consistent with sizes spanned by QSOs of lifetime tq ∼ 1–100 Myr in simulations. Due to the scatter created by the low signal-to-noise spectra and large Ly α redshift uncertainty, we cannot conclude whether the redshift evolution of the near-zone size is affected by the reionization effect.
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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) | 0.001 | 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".