利用多频时域分量分离减轻宇宙微波背景观测中的大气干扰
Atmosphere mitigation in CMB observations using multi-frequency time-domain component separation
浏览论文内容
中文总结 AI 辅助
该研究针对地面CMB观测受大气干扰问题，提出基于多频焦平面估计大气发射模板的新数据分析框架，用时域分量分离技术去除污染，引入净化因子量化效果，特定频段表现优于传统方法。
中文摘要 AI 辅助
地面天文台的宇宙微波背景（CMB）观测受地球大气波动辐射限制，主要源于水汽不均匀性。传统减轻干扰技术有低频滤波等，但存在问题。本文提出基于多频焦平面估计大气发射模板的数据分析框架，通过专用探测器监测大气，用时域分量分离技术去除污染。引入多极相关大气净化因子量化效果，证明其与观测时间成反比，在特定频段表现优于传统方法。
英文摘要
CMB observations from ground-based observatories are limited in sensitivity by the fluctuating emission from the Earth's atmosphere, mostly due to water vapor inhomogeneities. Even in atmospheric windows, this spurious signal remains the dominant source of contamination in the data. Traditional mitigation techniques include low-frequency filtering, or for polarization measurements specifically, pair-differencing or modulation with a rotating half-wave plate. The first method filters out a significant fraction of the target cosmological signal while the second leaves residuals due to imperfections, temperature to polarization leakage, or polarized atmospheric emission. In this work, we present a new data analysis framework, based on estimation of atmosphere emission templates using a multi-frequency focal plane. The core novelty of this setup is to have detectors dedicated to atmosphere monitoring, allowing the removal of atmospheric contamination with time-domain component separation techniques. We introduce a multipole-dependent atmospheric decontamination factor $A^\mathrm{atm}_\ell$ to quantify the relative reduction of the atmospheric contamination angular power spectrum achievable with this approach. Using this criterion, we demonstrate that our component separation pipeline can outperform a classical filter-bin map-making pipeline by a factor of 4000 for $30\le \ell \le 300$.
