分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： 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.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Jinyi Liu
Zeyang Sun
Jiakang Han
Julien Carron
Jacques Delabrouille
Siyu Li
Yang Liu
Jing Jin
Shamik Ghosh
Bin Yue
Pengjie Zhang
Chang Feng
Zhi-Qi Huang
Hao Liu
Yi-Wen Wu
Le Zhang
Zi-Rui Zhang
Wen Zhao
Bin Hu
Hong Li
摘要： AliCPT-1 is the first Chinese CMB experiment aiming for high precision
measurement of Cosmic Microwave Background B-mode polarization. The telescope,
currently under deployment in Tibet, will observe in two frequency bands
centered at 90 and 150 GHz. We forecast the CMB lensing reconstruction,
lensing-galaxy as well as lensing-CIB (Cosmic Infrared Background) cross
correlation signal-to-noise ratio (SNR) for AliCPT-1. We consider two stages
with different integrated observation time, namely "4 module*yr" (first stage)
and "48 module*yr" (final stage). For lensing reconstruction, we use three
different quadratic estimators, namely temperature-only, polarization-only and
minimum-variance estimators, using curved sky geometry. We take into account
the impact of inhomogeneous hit counts as well as of the mean-field bias due to
incomplete sky coverage. In the first stage, our results show that the 150 GHz
channel is able to measure the lensing signal at $15\sigma$ significance with
the minimum-variance estimator. In the final stage, the measurement
significance will increase to $31\sigma$. We also combine the two frequency
data in the harmonic domain to optimize the SNR. Our result show that the
coadding procedure can significantly reduce the reconstruction bias in the
multiple range l>800. Thanks to the high quality of the polarization data in
the final stage of AliCPT-1, the EB estimator will dominate the lensing
reconstruction in this stage. We also estimate the SNR of cross-correlations
between AliCPT-1 CMB lensing and other tracers of the large scale structure of
the universe. For its cross-correlation with DESI galaxies/quasars, we report
the cross-correlation SNR = 10-20 for the 4 redshift bins at 0.05
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： 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.
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