LAUSR

The atmosphere is one of the most significant sources of contamination in ground-based Cosmic Microwave Background (CMB) observations. Atmospheric emission increases the additional optical loading on the detector, resulting in higher photon noise. Additionally, atmospheric fluctuations cause spatial and temporal variations in detected power, leading to additional correlations between detectors and in the time stream of individual detectors. This correlated signal, known as the 1/f noise, can interfere with the detection of CMB signals, severely hindering the probing of CMB signals. In this paper, we study three types of filters: the polynomial fitting, high-pass filter, and Wiener filter. We evaluate the filters based on their ability to remove atmospheric noise, and investigate the impact of the filters on the data analytic process through end-to-end simulations of CMB experiments. We track their performance by analyzing the response of different components of the data, including both signal and noise. In the time domain, the high-pass filter is found to have the smallest root mean square error and achieves high filtering efficiency, followed by the Wiener filter and polynomial fitting. We adopt two map making methods, namely naive map making and Minimum Variance map making, to study the effects of filters on the map level. The results show that the polynomial fitting gives a high noise residual at low frequency, resulting in significant leakage to small scales in the map domain, while the high-pass and Wiener filters do not have significant leakage. We compare the filters' effects on the power spectra domain by estimating the angular power spectra of residual noise and input signal, and estimating the standard deviation of the signal recovered power spectra. At low noise level, the three filters give almost comparable standard deviations on medium and small scales. However, at high noise level, the standard deviation of the polynomial fitting is significantly larger. These studies can be used for reducing atmospheric noise in future ground-based CMB data processing.