Impact of anisotropic birefringence on measuring cosmic microwave background lensing
Bibliographic record
Abstract
The power spectrum of cosmic microwave background lensing is a powerful tool for constraining fundamental physics such as the sum of neutrino masses and the dark energy equation of state. Current lensing measurements primarily come from distortions to the microwave background temperature field, but the polarization lensing signal will dominate upcoming experiments with greater sensitivity. Cosmic birefringence refers to the rotation of the linear polarization direction of microwave photons propagating from the last scattering surface to us, which can be induced by parity-violating physics such as axionlike dark matter or primordial magnetic fields. We find that, for an upcoming CMB-S4-like experiment, scale-invariant anisotropic birefringence with an amplitude corresponding to the current 95% upper bound can bias the measured lensing power spectrum by up to a factor of a few at small scales, $L\ensuremath{\gtrsim}1000$. We show that the bias scales linearly with the amplitude of the scale-invariant birefringence spectrum. The signal to noise of the contribution from anisotropic birefringence is larger than unity even if the birefringence amplitude decreases to around 5% of the current upper bound. Our results indicate that measurement and characterization of possible anisotropic birefringence is important for lensing analysis in future low-noise polarization experiments.
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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 | 0.007 |
| 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 | 0.000 | 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".