论文标题
希格洛：高保真hi地图建模的条件标准化流
HIGlow: Conditional Normalizing Flows for High-Fidelity HI Map Modeling
论文作者
论文摘要
从即将进行的大规模调查中提取最大数量的宇宙学和天体物理信息仍然是一个挑战。这包括评估确切的可能性，参数推理和生成传入的高维数据集的新不同合成示例。在这项工作中，我们建议将归一化流程用作骆驼项目中立氢（HI）地图的生成模型。在参数推理和生成新的现实示例方面，归一化流非常成功。我们的模型利用了HI地图的空间结构，以忠实地遵循数据的统计数据，从而可以产生高保真样本和有效的参数推断。
Extracting the maximum amount of cosmological and astrophysical information from upcoming large-scale surveys remains a challenge. This includes evaluating the exact likelihood, parameter inference and generating new diverse synthetic examples of the incoming high-dimensional data sets. In this work, we propose the use of normalizing flows as a generative model of the neutral hydrogen (HI) maps from the CAMELS project. Normalizing flows have been very successful at parameter inference and generating new, realistic examples. Our model utilizes the spatial structure of the HI maps in order to faithfully follow the statistics of the data, allowing for high-fidelity sample generation and efficient parameter inference.
