Efficient estimation of rotation-induced bias to reconstructed CMB lensing power spectrum
Bibliographic record
Abstract
The cosmic microwave background (CMB) lensing power spectrum is a powerful probe of the late-time Universe, encoding valuable information about cosmological parameters such as the sum of neutrino masses and dark energy equation of state. However, the presence of anisotropic cosmic birefringence can bias the reconstructed CMB lensing power spectrum using CMB polarization maps, particularly at small scales, and affect the constraints on these parameters. Upcoming experiments, which will be dominated by the polarization lensing signal, are especially susceptible to this bias. We identify the dominant contribution to this bias as an ${N}_{L}^{(1)}$-like noise, caused by anisotropic rotation instead of lensing. We show that, for an CMB-S4-like experiment, a scale-invariant anisotropic rotation field with a standard deviation of 0.05 degrees can suppress the small-scale lensing power spectrum ($L\ensuremath{\gtrsim}2000$) at a comparable level to the effect of massive neutrino with ${\ensuremath{\sum}}_{i}{m}_{{\ensuremath{\nu}}_{i}}=50\text{ }\text{ }\mathrm{meV}$, making rotation field an important source of degeneracy in neutrino mass measurement for future CMB experiments. We provide an analytic expression and a simulation-based estimator for this ${N}_{L}^{(1)}$-like noise, which allows for efficient forecasting and mitigation of the bias in future experiments. Furthermore, we investigate the impact of a non-scale-invariant rotation power spectrum on the reconstructed lensing power spectrum and find that an excess of power in the small-scale rotation power spectrum leads to a larger bias. Our work provides an effective numeric framework to accurately model and account for the bias caused by anisotropic rotation in future CMB lensing measurements.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".