Minihalo photoevaporation during cosmic reionization: evaporation times and photon consumption rates
Bibliographic record
Abstract
The weak, R-type ionization fronts (I-fronts) which swept across the intergalactic medium during the reionization of the Universe often found their paths blocked by cosmological minihaloes (haloes with virial temperatures Tvir≤ 104 K). When this happened, the neutral gas which filled each minihalo was photoevaporated. In a cold dark matter universe, minihaloes formed in abundance before and during reionization and, thus, their photoevaporation is an important, possibly dominant, feature of reionization, which slowed it down and cost it many ionizing photons. In a previous paper, we described this process and presented our results of the first simulations of it by numerical gas dynamics with radiation transport in detail. In view of the importance of minihalo photoevaporation, both as a feedback mechanism on the minihaloes and as an effect on cosmic reionization, we have now performed a larger set of high-resolution simulations to determine and quantify the dependence of minihalo photoevaporation times and photon consumption rates on halo mass, redshift, ionizing flux level and spectrum. We use these results to derive simple expressions for the dependence of the evaporation time and photon consumption rate on these halo and external flux parameters. These can be conveniently applied to estimate the effects of minihaloes on the global reionization process in both semi-analytical calculations and larger-scale, lower-resolution numerical simulations, which cannot adequately resolve the minihaloes and their photoevaporation. We find that the average number of ionizing photons each minihalo atom absorbs during its photoevaporation is typically in the range 2–10. For the collapsed fraction in minihaloes expected during reionization, this can add about one photon per total atom to the requirements for completing reionization, potentially doubling the minimum number of photons required to reionize the Universe.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".