Cosmic Reionization on Computers: The Evolution of the Ionizing Background and Mean Free Path
Bibliographic record
Abstract
Abstract Observations of the end stages of reionization indicate that at z ≈ 5–6, the ionizing background is not uniform and the mean free path (MFP) changes drastically. As the MFP is closely related to the distribution of Lyman-limit systems (LLSs) and damped Lyα absorbers, it is important to understand them. In this study, we utilize the Cosmic Reionization on Computers (CROC) simulations, which have both sufficient spatial resolution to resolve galaxy formation and LLSs alongside a fully coupled radiative transfer, to simulate the reionization processes. We analyze two CROC boxes with distinct reionization histories and find that the distributions of the ionizing background in both simulations display significant skewness. Further, the ionizing background in the late-reionization box still displays significant fluctuations (∼40%) at z ≈ 5. We also measure the MFP along sightlines that center on potential quasar hosting halos. The evolution of the MFP measured from these sightlines exhibits a break that coincides with the disappearance of all the neutral islands in the reionization history of each box. In the absence of LLSs, the MFP will be biased high by ≈20% at z ≈ 5. We also compare the MFPs measured in random sightlines. We find that at z ≈ 5, the MFPs measured in sightlines that start from massive halos are systematically smaller by ≈10% compared with the MFPs measured in random sightlines. We attribute this difference to the concentration of dense structures within 1 pMpc of massive halos. Our findings highlight the importance of high-fidelity models in the interpretation of observational measurements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".