Simulating cosmic reionization at large scales - II. The 21-cm emission features and statistical signals
Bibliographic record
Abstract
We present detailed predictions for the redshifted 21-cm signal from the epoch of reionization. These predictions are obtained from radiative transfer calculations on the results of large-scale (100 h−1 Mpc), high dynamic range, cosmological simulations. We consider several scenarios for the reionization history, of both early and extended reionizations. From the simulations, we construct and analyse a range of observational characteristics, from the global signal, via detailed images and spectra, to statistical representations of rms fluctuations, angular power spectra, and probability distribution functions to characterize the non-Gaussianity of the 21-cm signal. We find that the different reionization scenarios produce quite similar observational signatures, mostly differing in the redshifts of 50 per cent reionization, and of final overlap. All scenarios show a gradual transition in the global signatures of mean signal and rms fluctuations, which would make these more difficult to observe. Individual features, such as deep gaps and bright peaks, are substantially different from the mean, and mapping these with several arcminutes and 100 s of kHz resolution would provide a direct measurement of the underlying density field and the geometry of the cosmological H ii regions, although significantly modified by peculiar velocity distortions. The presence of late emission peaks suggests these to be a useful target for observations. The power spectra during reionization are strongly boosted compared to the underlying density fluctuations. The strongest statistical signal is found around the time of 50 per cent reionization and displays a clear maximum at an angular scale of ℓ∼ 3000–5000. We find the distribution function of emission features to be strongly non-Gaussian, with an order of magnitude higher probability of bright emission features. These results suggest that, observationally, it may be easier to find individual bright features than deriving the power spectra, which, in turn, is easier than observing individual images.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".