论文标题
从莱曼（Lyman
Predicting 21cm-line map from Lyman $α$ emitter distribution with Generative Adversarial Networks
论文作者
论文摘要
从回离时期（EOR）对21 \ 21 \，CM线信号的无线电观察使我们能够探索早期宇宙中星系和播层间介质的演变。但是，由于前景和仪器系统学，对21 \的检测和成像很难。为了克服这些障碍，作为一种新方法，我们建议在观察到的21 \，CM线数据与21 \，21 \，CM线图像之间进行交叉相关性，该图像是通过机器学习通过Lyman-$ lyman- $ laes Emitters（Laes）的分布产生的。为了从LAE分布创建21 \，CM线图，我们应用了经过数值模拟结果训练的条件生成对抗网络（CGAN）。我们发现21 \ 21 \ cm-line亮度温度图和中性分数图可以通过0.5在大尺度下的相关函数$ k <0.1〜 {\ rm mpc}^{ - 1} $复制。此外，我们研究了Subaru Hyper Soprime Cam的LAE深度调查，MWA II期的21 \，CM观察以及前景残留物的存在，我们研究了跨相关性的可检测性。我们表明，即使前景残差比21 \ 21 \ 21 \，CM-line Power Spectrum大5倍，也可以在$ K <0.1〜 {\ rm MPC}^{ - 1} $中检测到信号。我们使用CGAN与图像构建的跨相关性的新方法不仅可以提高EOR 21 \，CM线信号的可检测性，而且还可以估算21 \，CM线自动启动频谱。
The radio observation of 21\,cm-line signal from the Epoch of Reionization (EoR) enables us to explore the evolution of galaxies and intergalactic medium in the early universe. However, the detection and imaging of the 21\,cm-line signal are tough due to the foreground and instrumental systematics. In order to overcome these obstacles, as a new approach, we propose to take a cross correlation between observed 21\,cm-line data and 21\,cm-line images generated from the distribution of the Lyman-$α$ emitters (LAEs) through machine learning. In order to create 21\,cm-line maps from LAE distribution, we apply conditional Generative Adversarial Network (cGAN) trained with the results of our numerical simulations. We find that the 21\,cm-line brightness temperature maps and the neutral fraction maps can be reproduced with correlation function of 0.5 at large scales $k<0.1~{\rm Mpc}^{-1}$. Furthermore, we study the detectability of the the cross correlation assuming the the LAE deep survey of the Subaru Hyper Suprime Cam, the 21\,cm observation of the MWA Phase II and the presence of the foreground residuals. We show that the signal is detectable at $k < 0.1~{\rm Mpc}^{-1}$ with 1000 hours of MWA observation even if the foreground residuals are 5 times larger than the 21\,cm-line power spectrum. Our new approach of cross correlation with image construction using the cGAN can not only boost the detectability of EoR 21\,cm-line signal but also allow us to estimate the 21\,cm-line auto-power spectrum.
