REIONIZATION ON LARGE SCALES. II. DETECTING PATCHY REIONIZATION THROUGH CROSS-CORRELATION OF THE COSMIC MICROWAVE BACKGROUND
Bibliographic record
Abstract
We investigate the effect of patchy reionization on the cosmic microwave background (CMB) temperature. An anisotropic optical depth alters the TT power spectrum on small scales l > 2000. We make use of the correlation between the matter density and the reionization redshift fields to construct full sky maps of . Patchy reionization transfers CMB power from large scales to small scales, resulting in a non-zero cross correlation between large and small angular scales. We show that the patchy τ correlator is sensitive to small root mean square (rms) values τ rms ∼ 0.003 seen in our maps. We include frequency-independent secondaries such as CMB lensing and kinetic Sunyaev–Zel'dovich (kSZ) terms, and show that patchy τ may still be detected at high significance. Reionization models that predict different values of τ rms may be distinguished even for the same mean value 〈τ〉. It is more difficult to detect patchy τ in the presence of larger secondaries such as the thermal Sunyaev–Zel'dovich, radio background, and the cosmic infrared background. In this case, we show that patchy τ may be detected if these frequency-dependent secondaries are minimized to ≲ 5 μK (rms) by means of a multi-frequency analysis. We show that the patchy τ correlator provides information that is complementary to what may be obtained from the polarization and the kSZ power spectra.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 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".