We propose a deep learning analysis technique with a 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 = 6–13. We then apply two observational effects, such as instrumental noise and limit of (spatial and depth) resolution somewhat suitable for realistic choices of the Square Kilometre Array (SKA), into the 21-cm maps. 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 great agreement with the true value even after coarsely smoothing with broad beam size and frequency bandwidth and heavily covered by noise with narrow beam size and frequency bandwidth. Our results show that the deep learning analyzing method has the potential to reconstruct the EoR history efficiently from the 21-cm tomography surveys in future.

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再电离时期 21 厘米差亮度温度的深度学习研究

我们提出了一种带有卷积神经网络 (CNN) 的深度学习分析技术，以从 21 厘米差分亮度温度断层扫描图像预测再电离时代 (EoR) 的进化轨迹。我们使用 21cmFAST，一种快速半数字宇宙学 21 厘米信号模拟器，在 z = 6-13 之间生成模拟 21 厘米地图。然后，我们将两种观测效果应用到 21 厘米地图中，例如仪器噪声和（空间和深度）分辨率限制，这在某种程度上适合于平方千米阵列 (SKA) 的实际选择。我们使用 CNN 设计我们的深度学习模型，以从给定的 21 厘米地图中预测切片平均的中性氢分数。即使在使用宽波束尺寸和频率带宽进行粗略平滑并且被窄波束尺寸和频率带宽的噪声严重覆盖后，我们的 CNN 模型估计的中性分数与真实值也有很大的一致性。我们的结果表明，深度学习分析方法有可能在未来从 21 厘米断层扫描调查中有效地重建 EoR 历史。