Inferring reionization and galaxy properties from the patchy kinetic Sunyaev–Zel’dovich signal
Bibliographic record
Abstract
ABSTRACT The patchy kinetic Sunyaev–Zel’dovich (kSZ) signal is an integral probe of the timing and morphology of the epoch of reionization (EoR). Recent observations have claimed a low signal-to-noise (S/N) measurement, with a dramatic increase in S/N expected in the near future. In this work, we quantify what we can learn about the EoR from the kSZ signal. We perform Bayesian inference by sampling galaxy properties and using forward-models of the kSZ as well as other EoR and galaxy observations in the likelihood. Including the recent kSZ measurement obtained by the South Pole Telescope ($\mathcal {D}_{3000}^{\textrm {pkSZ}} = 1.1_{-0.7}^{+1.1} \mu$ K2) shifts the posterior distribution in favour of faster and later reionization models, resulting in lower values of the optical depth to the cosmic microwave background: $\tau _e = 0.052_{-0.008}^{+0.009}$ with a 68 per cent confidence interval (CI). The combined EoR and ultraviolet luminosity function observations also imply a typical ionizing escape fraction of $0.04_{-0.03}^{+0.05}$ (95 per cent CI), without a strong dependence on halo mass. We show how the patchy kSZ power from our posterior depends on the midpoint and duration of reionization: a popular parametrization of EoR timing. For a given midpoint and duration, the EoR morphology only has a few per cent impact on the patchy kSZ power in our posterior. However, a physical model is needed to obtain tight constraints from the current low S/N patchy kSZ measurement, as it allows us to take advantage of complimentary high-z observations. Future high S/N detections of the patchy kSZ should decrease the current uncertainties on the timing of the EoR by factors of ∼2–3.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".