Spatial fluctuations of the intergalactic temperature–density relation after hydrogen reionization
Bibliographic record
Abstract
The thermal state of the post-reionization IGM is sensitive to the timing of reionization and the nature of the ionizing sources. We have modelled here the thermal state of the IGM in cosmological radiative transfer simulations of a realistic, extended, spatially inhomogeneous hydrogen reionization process, carefully calibrated with Ly α forest data. We compare these with cosmological simulations run using a spatially homogeneous ionizing background. The simulations with a realistic growth of ionized regions and a realistic spread in reionization redshifts show, as expected, significant spatial fluctuations in the temperature–density relation (TDR) of the post-reionization IGM. The most recently ionized regions are hottest and exhibit a flatter TDR. In simulations consistent with the average TDR inferred from Ly α forest data, these spatial fluctuations have a moderate but noticeable effect on the statistical properties of the Ly α opacity of the IGM at z ∼ 4–6. This should be taken into account in accurate measurements of the thermal properties of the IGM and the free-streaming of dark matter from Ly α forest data in this redshift range. The spatial variations of the TDR predicted by our simulations are, however, smaller by about a factor of 2 than would be necessary to explain the observed large spatial opacity fluctuations on large (≥50 h−1 comoving Mpc) scales atz ≳ 5.5.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".