4.7 Article

Testing Cosmic Microwave Background Anomalies in E-mode Polarization with Current and Future Data

Journal

ASTROPHYSICAL JOURNAL
Volume 945, Issue 1, Pages -

Publisher

IOP Publishing Ltd
DOI: 10.3847/1538-4357/acb339

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This paper investigates the use of cosmic microwave background (CMB) polarization data to confirm anomalies in CMB temperature data. It finds that the E-mode estimators provide a largely independent check on these anomalies, with correlation coefficients between temperature and E-mode estimators less than 0.1. The study also shows that future experiments like the LiteBIRD survey can significantly reduce errors in anomaly estimators, particularly for the lack of large-scale correlation. The improvement in other anomalies like Q-O alignment is less clear due to cosmic variance.
In this paper, we explore the power of the cosmic microwave background (CMB) polarization (E-mode) data to corroborate four potential anomalies in CMB temperature data: the lack of large angular-scale correlations, the alignment of the quadrupole and octupole (Q-O), the point-parity asymmetry, and the hemispherical power asymmetry. We use CMB simulations with noise representative of three experiments-the Planck satellite, the Cosmology Large Angular Scale Surveyor (CLASS), and the LiteBIRD satellite-to test how current and future data constrain the anomalies. We find the correlation coefficients rho between temperature and E-mode estimators to be less than 0.1, except for the point-parity asymmetry (rho = 0.17 for cosmic-variance-limited simulations), confirming that E-modes provide a check on the anomalies that is largely independent of temperature data. Compared to Planck component-separated CMB data (smica), the putative LiteBIRD survey would reduce errors on E-mode anomaly estimators by factors of similar to 3 for hemispherical power asymmetry and point-parity asymmetry, and by similar to 26 for lack of large-scale correlation. The improvement in Q-O alignment is not obvious due to large cosmic variance, but we found the ability to pin down the estimator value will be improved by a factor greater than or similar to 100. Improvements with CLASS are intermediate to these.

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