Study on the filters of atmospheric contamination in ground based CMB observation

TL;DR

This study compares polynomial, high-pass, and Wiener filters for removing atmospheric noise in ground-based CMB observations, analyzing their effectiveness and impact on data analysis through simulations and power spectrum evaluations.

Contribution

It provides a comprehensive comparison of three filtering techniques and their effects on CMB data quality, guiding future atmospheric noise reduction strategies.

Findings

High-pass filter has the smallest RMSE and highest filtering efficiency.

Polynomial filter causes significant low-frequency noise residual and leakage.

At high noise levels, polynomial filter's standard deviation increases notably.

Abstract

The atmosphere is one of the most important contamination sources in the ground-based Cosmic Microwave Background (CMB) observations. In this paper, we study three kinds of filters, which are polynomial filter, high-pass filter, and Wiener filter, to investigate their ability for removing atmospheric noise, as well as their impact on the data analysis process through the end-to-end simulations of CMB experiment. We track their performance by analyzing the response of different components of the data, including both signals and noise. In the time domain, the calculation shows that the high-pass filter has the smallest root mean square error and can achieve high filtering efficiency, followed by the Wiener filter and polynomial filter. We then perform map-making with the filtered time ordered data (TOD) to trace the effects from filters on the map domain, and the results show that the polynomial filter gives high noise residual at low frequency, which gives rise to serious leakage to small scales in map domain during the map-making process, while the high-pass filter and Wiener filter do not have such significant leakage. Then we estimate the angular power spectra of residual noise, as well as those of the input signal for comparing the filter effects in the power spectra domain. Finally, we estimate the standard deviation of the filter corrected power spectra to compare the effects from different filters, and the results show that, at low noise level, the three filters give almost comparable standard deviations on the medium and small scales, but at high noise level, the standard deviation of the polynomial filter is significantly larger. These studies can be used for the reduction of atmospheric noise in future ground-based CMB data processing.