论文标题
通过高斯过程回归的前景建模：HERA数据的应用
Foreground modelling via Gaussian process regression: an application to HERA data
论文作者
论文摘要
观察从宇宙电离的红移21厘米信号的关键挑战是它与更明亮的前景发射的分离。这种分离依赖于两个组成部分的不同光谱特性，尽管在现实生活中，前景固有光谱通常会因仪器响应而损坏，从而诱导系统效应，从而进一步危害21 cm信号的测量。在本文中，我们使用高斯工艺回归对$ \ sim中的前景发射和仪器系统进行建模，以$ \ sim 2 $小时的氢气时期数据。我们发现，具有三个组件的简单共同变异模型很好地匹配了数据，从而提供了具有白噪声属性的残留功率谱。这些由一个“固有”且具有乐器损坏的组件组成，其相干尺度分别为20 MHz和2.4 MHz（在尺度上占主导地位的视线功能谱$ k _ {\ parallel} \ le 0.2 $ h cmpc $^{-1} $）和基线依赖于$ $ $ $ $ $ $ \ MH， $ k _ {\ Parallel} \ sim 0.4-0.8 $ h cmpc $^{ - 1} $，应与21厘米EOR信号区分开，其典型的相干 - 尺度为$ \ sim 0.8 $ MHz。
The key challenge in the observation of the redshifted 21-cm signal from cosmic reionization is its separation from the much brighter foreground emission. Such separation relies on the different spectral properties of the two components, although, in real life, the foreground intrinsic spectrum is often corrupted by the instrumental response, inducing systematic effects that can further jeopardize the measurement of the 21-cm signal. In this paper, we use Gaussian Process Regression to model both foreground emission and instrumental systematics in $\sim 2$ hours of data from the Hydrogen Epoch of Reionization Array. We find that a simple co-variance model with three components matches the data well, giving a residual power spectrum with white noise properties. These consist of an "intrinsic" and instrumentally corrupted component with a coherence-scale of 20 MHz and 2.4 MHz respectively (dominating the line of sight power spectrum over scales $k_{\parallel} \le 0.2$ h cMpc$^{-1}$) and a baseline dependent periodic signal with a period of $\sim 1$ MHz (dominating over $k_{\parallel} \sim 0.4 - 0.8$h cMpc$^{-1}$) which should be distinguishable from the 21-cm EoR signal whose typical coherence-scales is $\sim 0.8$ MHz.
