用深度学习从射电数据中提取早期宇宙氢再电离历史，精度超95%。
Exploring the Early Universe with Deep Learning
- 设计专用神经网络处理SKAO的二维功率谱数据
- 平均$R^2$达0.95，精准还原再电离历史
- 适合研究早期星系与宇宙结构形成的科研人员
氢是宇宙中最丰富的元素。第一代恒星和星系产生的光子电离了氢气，引发称为再电离时代（EoR）的宇宙事件。即将建成的平方公里阵列天文台（SKAO）将绘制该时期中性氢的分布，助力研究第一代天体的性质。然而，SKAO将产生海量数据，其中氢信号受到前景污染和仪器系统误差的严重干扰。为此，我们采用最新的深度学习技术，对SKAO预期的氢信号二维功率谱进行分析。通过一系列神经网络模型，量化其预测宇宙氢再电离历史的能力，该历史与早期光子源的数量和效率增长密切相关。结果表明，现代深度学习技术显著提升了早期宇宙研究的潜力。特别地，专用机器学习算法在恢复再电离历史方面平均$R^2$得分超过0.95，实现了对早期宇宙结构形成过程的精确、可靠的宇宙学与天体物理推断。
原文摘要 · Abstract (English)
Hydrogen is the most abundant element in our Universe. The first generation of stars and galaxies produced photons that ionized hydrogen gas, driving a cosmological event known as the Epoch of Reionization (EoR). The upcoming Square Kilometre Array Observatory (SKAO) will map the distribution of neutral hydrogen during this era, aiding in the study of the properties of these first-generation objects. Extracting astrophysical information will be challenging, as SKAO will produce a tremendous amount of data where the hydrogen signal will be contaminated with undesired foreground contamination and instrumental systematics. To address this, we develop the latest deep learning techniques to extract information from the 2D power spectra of the hydrogen signal expected from SKAO. We apply a series of neural network models to these measurements and quantify their ability to predict the history of cosmic hydrogen reionization, which is connected to the increasing number and efficiency of early photon sources. We show that the study of the early Universe benefits from modern deep learning technology. In particular, we demonstrate that dedicated machine learning algorithms can achieve more than a $0.95$ $R^2$ score on average in recovering the reionization history. This enables accurate and precise cosmological and astrophysical inference of structure formation in the early Universe.
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