THE KINETIC SUNYAEV–ZEL’DOVICH EFFECT FROM REIONIZATION: SIMULATED FULL-SKY MAPS AT ARCMINUTE RESOLUTION
Bibliographic record
Abstract
ABSTRACT The kinetic Sunyaev–Zel’dovich (kSZ) effect results from Thomson scattering by coherent flows in the reionized intergalactic medium. We present new results based on ray-tracing an 8 Gpc/h realization of reionization with resolution elements 2 Mpc/h (subtending ′ at z = 6) on a side to create a full-sky kSZ map. The realization includes, self-consistently, the effects of reionization on scales corresponding to multipoles . We separate the kSZ map into Doppler ( ), Ostriker–Vishniac ( ), patchy ( ), and third-order ( ) components, and compute explicitly all the auto- and cross-correlations (e.g., , , etc.) that contribute to the total power. We find a complex and nonmonotonic dependence on the duration of reionization at and evidence for a non-negligible (10%–30%) contribution from connected four-point correlations, , usually neglected in analytical models. We also investigate the cross-correlation of linear matter and large-scale kSZ temperature fluctuations, focusing on (1) cross-power spectra with biased tracers of the matter density and (2) cold spots from infall onto large, rare H ii regions centered on peaks in the matter distribution at redshifts that are a generic non-Gaussian feature of patchy reionization. Finally, we show that the reionization history can be reconstructed at 5σ–10σ significance by correlating full-sky 21 cm maps stacked in bins with with existing cosmic microwave background (CMB) temperature maps at , raising the prospects for probing reionization by correlating CMB and LSS measurements. The resulting kSZ maps have been made publicly available at www.cita.utoronto.ca/~malvarez/research/ksz-data/ .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".