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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 ρ between temperature and E-mode estimators to be less than 0.1, except for the point-parity asymmetry (ρ = 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 ∼3 for hemispherical power asymmetry and point-parity asymmetry, and by ∼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 ≳100. Improvements with CLASS are intermediate to these.

The Cosmology Large Angular Scale Surveyor (CLASS) observes the polarized cosmic microwave background (CMB) over the angular scales of 1$^\circ \lesssim \theta \leq$ 90$^\circ$ with the aim of characterizing primordial gravitational waves and cosmic reionization. We report on the on-sky performance of the CLASS Q-band (40 GHz), W-band (90 GHz), and dichroic G-band (150/220 GHz) receivers that have been operational at the CLASS site in the Atacama desert since June 2016, May 2018, and September 2019, respectively. We show that the noise-equivalent power measured by the detectors matches the expected noise model based on on-sky optical loading and lab-measured detector parameters. Using Moon, Venus, and Jupiter observations, we obtain power-to-antenna-temperature calibrations and optical efficiencies for the telescopes. From the CMB survey data, we compute instantaneous array noise-equivalent-temperature sensitivities of 22, 19, 23, and 71 $\mathrm{\mu K}_\mathrm{cmb}\sqrt{\mathrm{s}}$ for the 40, 90, 150, and 220 GHz frequency bands, respectively. These noise temperatures refer to white noise amplitudes, which contribute to sky maps at all angular scales. Future papers will assess additional noise sources impacting larger angular scales., Comment: 13 pages, 3 figures, published in ApJ

The current and future cosmic microwave background (CMB) experiments fielding kilo-pixel arrays of transition-edge sensor (TES) bolometers require accurate and robust gain calibration methods. We simplify and refactor the standard TES model to directly relate the detector responsivity calibration and optical time constant to the measured TES current $I$ and the applied bias current $I_{\mathrm{b}}$. The calibration method developed for the Cosmology Large Angular Scale Surveyor (CLASS) TES bolometer arrays relies on current versus voltage ($I$-$V$) measurements acquired daily prior to CMB observations. By binning Q-band (40GHz) $I$-$V$ measurements by optical loading, we find that the gain calibration median standard error within a bin is 0.3%. We test the accuracy of this "$I$-$V$ bin" detector calibration method by using the Moon as a photometric standard. The ratio of measured Moon amplitudes between detector pairs sharing the same feedhorn indicates a TES calibration error of 0.5%. We also find that for the CLASS Q-band TES array, calibrating the response of individual detectors based solely on the applied TES bias current accurately corrects TES gain variations across time but introduces a bias in the TES calibration from data counts to power units. Since the TES current bias value is set and recorded before every observation, this calibration method can always be applied to raw TES data and is not subject to $I$-$V$ data quality or processing errors., Comment: 19 pages, 7 figures, 5 tables. Submitted to ApJS May 2022. Published ApJS Oct 2022