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.

翻译：氢是宇宙中含量最丰富的元素。第一代恒星和星系产生的光子电离了氢气，驱动了被称为再电离纪元（EoR）的宇宙学事件。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图，有助于研究这些第一代天体的性质。提取天体物理信息将面临挑战，因为SKAO将产生海量数据，其中氢信号会受到不期望的前景污染和仪器系统误差的影响。为此，我们开发了最新的深度学习技术，从SKAO预期的氢信号二维功率谱中提取信息。我们对这些测量数据应用了一系列神经网络模型，并量化了它们预测宇宙氢再电离历史的能力，该历史与早期光子源数量及效率的增长相关。我们表明，对早期宇宙的研究受益于现代深度学习技术。特别地，我们证明了专用的机器学习算法在恢复再电离历史时平均可获得超过$0.95$的$R^2$分数。这使得对早期宇宙结构形成的宇宙学和天体物理推断能够达到高精度与高准确度。