标题： 从拓扑学中学习：大尺度结构的宇宙参数估计 显示英文标题

标题： Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure

摘要： 宇宙大尺度结构的拓扑包含有关潜在宇宙学参数的有价值信息。尽管持久同调可以提取这种拓扑信息，但从该工具进行参数估计的最佳方法仍然是一个悬而未决的问题。为了解决这个问题，我们提出了一种神经网络模型，将持久图像映射到宇宙学参数。通过参数恢复测试，我们证明我们的模型能够做出准确且精确的估计，明显优于传统的贝叶斯推理方法。 显示英文摘要

摘要： The topology of the large-scale structure of the universe contains valuable information on the underlying cosmological parameters. While persistent homology can extract this topological information, the optimal method for parameter estimation from the tool remains an open question. To address this, we propose a neural network model to map persistence images to cosmological parameters. Through a parameter recovery test, we demonstrate that our model makes accurate and precise estimates, considerably outperforming conventional Bayesian inference approaches.