论文标题
在电离时期的21厘米差异亮度温度的深度学习研究
Deep-Learning Study of the 21cm Differential Brightness Temperature During the Epoch of Reionization
论文作者
论文摘要
我们提出了一种使用卷积神经网络（CNN）的深度学习分析技术，以预测21-CM差异亮度温度层析成像图像中电离时代（EOR）的进化轨道。我们使用21cmfast，这是一种快速的半数字宇宙学信号模拟器，在$ z = 6 \ sim 13 $之间生成模拟21厘米地图。然后，我们将两种观察效应应用于21厘米地图，例如仪器噪声和（空间和深度）分辨率的极限，在某种程度上适合于平方公里阵列（SKA）的现实选择。我们使用CNN设计深度学习模型，以预测给定21厘米地图的切片平均中性氢数。我们CNN模型的估计中性分数与其真实值具有很大的一致性，即使在宽光束和频率带宽方面进行了粗糙，也被窄噪声覆盖。我们的结果表明，深度学习分析方法具有巨大的潜力，可以在将来从21-CM层析成像调查中有效地重建EOR病史。
We propose a deep learning analyzing technique with convolutional neural network (CNN) to predict the evolutionary track of the Epoch of Reionization (EoR) from the 21-cm differential brightness temperature tomography images. We use 21cmFAST, a fast semi-numerical cosmological 21-cm signal simulator, to produce mock 21-cm maps between $z=6 \sim 13$. We then apply two observational effects into those 21-cm maps, such as instrumental noise and limit of (spatial and depth) resolution somewhat suitable for realistic choices of the Square Kilometre Array (SKA). We design our deep learning model with CNN to predict the sliced-averaged neutral hydrogen fraction from the given 21-cm map. The estimated neutral fraction from our CNN model has a great agreement with its true value even after coarsely smoothing with broad beamsize and frequency bandwidth, and also heavily covered by noise with narrow. Our results have shown that deep learning analyzing method has a large potential to efficiently reconstruct the EoR history from the 21-cm tomography surveys in future.
