Bias to cosmic microwave background lensing reconstruction from the kinematic Sunyaev-Zel’dovich effect at reionization
Bibliographic record
Abstract
The power spectrum of reconstructed cosmic microwave background (CMB) lensing maps is a powerful tool for constraints on cosmological parameters like the sum of the neutrino masses and the dark energy equation of state. One possible complication is the kinematic Sunyaev-Zel'dovich (kSZ) effect, due to the scattering of CMB photons by moving electrons, which can bias the reconstruction of the CMB lensing power spectrum through both kSZ-lensing correlations and the non-Gaussianity of the kSZ temperature anisotropies. We investigate for the first time the bias to CMB lensing reconstruction from temperature anisotropies due to the reionization-induced kSZ signal and show that it is negligible for both ongoing and upcoming experiments based on current numerical simulations of reionization. We also revisit the bias induced by the late-time kSZ field, using more recent kSZ simulations. We find that it is potentially twice as large as found in earlier studies, reaching values as large as several percent of the CMB lensing power spectrum signal, indicating that this bias will have to be mitigated in upcoming data analyses.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".