CLASS Data Pipeline and Maps for 40 GHz Observations through 2022

TL;DR

This paper details the CLASS 40 GHz data pipeline and maps from 2016-2022, demonstrating effective large-scale polarization recovery and analyzing systematic errors for cosmic microwave background studies.

Contribution

It introduces the CLASS data pipeline and maps for 40 GHz observations, including polarization recovery performance and systematic error analysis.

Findings

Recovered ~75% of large-scale polarization power at =20

Maps have a white noise level of 110 bcK arcmin

Systematic biases are sub-percent for bCDM EE spectra

Abstract

The Cosmology Large Angular Scale Surveyor (CLASS) is a telescope array that observes the cosmic microwave background over 75\% of the sky from the Atacama Desert, Chile, at frequency bands centered near 40, 90, 150, and 220~GHz. This paper describes the CLASS data pipeline and maps for 40~GHz observations conducted from August 2016 to May 2022. We demonstrate how well the CLASS survey strategy, with rapid ($\sim10\,\mathrm{Hz}$) front-end modulation, recovers the large-scale Galactic polarization signal from the ground: the mapping transfer function recovers $\sim75$\% of $EE$, $BB$, and $VV$ power at $\ell=20$ and $\sim45$\% at $\ell=10$. We present linear and circular polarization maps over 75\% of the sky. Simulations based on the data imply the maps have a white noise level of $110\,\mathrm{\mu K\, arcmin}$ and correlated noise component rising at low-$\ell$ as $\ell^{-2.2}$. The transfer-function-corrected low-$\ell$ component is comparable to the white noise at the angular knee frequencies of $\ell\approx16$ (linear polarization) and $\ell\approx12$ (circular polarization). Finally, we present simulations of the level at which expected sources of systematic error bias the measurements, finding sub-percent bias for the $\Lambda\mathrm{CDM}$ $EE$ power spectra. Bias from $E$-to-$B$ leakage due to the data reduction pipeline and polarization angle uncertainty approaches the expected level for an $r=0.01$ $BB$ power spectrum. Improvements to the instrument calibration and the data pipeline will decrease this bias.