Constraining the epoch of reionization with highly dispersed fast radio bursts
Bibliographic record
Abstract
ABSTRACT The period in which hydrogen in the intergalactic medium (IGM) is ionized, known as the epoch of reionization (EoR), is still poorly understood. The timing and duration of the EoR is expected to be governed by the underlying astrophysics. Furthermore, most models of reionization predict a correlation between the density and ionization field. Here we consider using the mean dispersion measure (DM) of high redshift fast radio bursts (FRBs) as a probe of the underlying astrophysics and morphology of the EoR. To do this, we forecast observational scenarios by building mock data sets of non-repeating FRBs between redshifts 8 ≤ z ≤ 10. In our forecasts we assume a fiducial inside-out reionization scenario with midpoint of reionziation z = 2.0, and duration (i.e. the duration between mean ionized fraction of 0.25 to 0.75) Δz = 7.8. It is assumed that all FRBs have accompanying spectroscopic redshift measurements. We find that samples of 100 high redshift FRBs, in the above mentioned narrow redshift range, can rule out uncorrelated reionization at 68 per cent credibility, while larger samples, ≥104 FRBs, can rule out uncorrelated reionization at $95{{\ \rm per\ cent}}$ credibility. We also find 100 high redshift FRBs can rule out scenarios where the Universe is entirely neutral at z = 10 with $68{{\ \rm per\ cent}}$ credibility. Further, with ≥105 FRBs, we can constrain the duration Δz of reionization to $\Delta z = 2.0^{+0.5}_{-0.4}$ and the midpoint of reionization to $z = 7.8^{+0.4}_{-0.2}$ at 95 per cent credibility.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".