AI 中文总结
本研究训练卷积神经网络，从当今物质密度场成功预测宇宙平均宇宙学参数，证明可通过机器学习从晚期物质分布提取宇宙平均历史信息，为约束宇宙大尺度演化提供新途径。
AI 中文摘要
本工作研究机器学习是否可用于直接从当今物质密度分布推断平均宇宙学量。我们采用简化静默宇宙框架的实现，生成包含92610个独立相对论性简化宇宙学模拟的数据集，涵盖一系列初始条件和平均宇宙学参数。我们训练一个卷积神经网络，以当今物质密度场为输入，预测初始时刻和当前时刻的平均物质密度、宇宙学常数密度、曲率密度、运动学反作用密度参数以及哈勃参数。该网络对所有预测量的决定系数均超过0.9，误差分布显示仅小部分预测达到百分级或更高误差。尽管我们使用的简化静默宇宙近似限制了训练模型直接应用于观测数据，但结果提供了原理性证明：神经网络可仅从晚期物质分布成功恢复不同时期的平均宇宙学性质，包括反作用。更广泛而言，我们的发现表明，宇宙的平均宇宙学历史信息编码在当今密度场中，可通过机器学习技术提取。这为将类似方法应用于更现实的宇宙学模拟（如N-body模拟）和弱引力透镜图等观测探针开辟了可能性，最终为约束宇宙的大尺度演化提供了新途径。
英文摘要
In this work, we investigate whether machine learning can be used to infer averaged cosmological quantities directly from the present day matter density distribution. Using an implementation of the simplified silent universe framework, we generate a dataset consisting of 92610 independent relativistic, simplified cosmological simulations spanning a range of initial conditions and average cosmological parameters. We train a convolutional neural network to take the present-time matter density field as input and predict the averaged matter, cosmological constant, curvature, and kinematical backreaction density parameters as well as the Hubble parameter, at both initial and present time. The network achieves coefficients of determination exceeding 0.9 for all predicted quantities, with error distributions showing that only a small fraction of predictions reach percent-level errors or above. Although our use of the simplified silent universe approximation precludes direct application of the trained model to observational data, the results provide a proof-of-principle that neural networks can successfully recover averaged cosmological properties, including backreaction, at different epochs, simply from the late-time matter distribution. More broadly, our findings demonstrate that information about the averaged cosmological history of a universe is encoded in its present-time density field and can be extracted using machine learning techniques. This opens the possibility of applying similar approaches to more realistic cosmological simulations and observational probes such as N-body simulations and weak-lensing maps, ultimately providing a new avenue for constraining the large-scale evolution of the Universe.
Comments11 pages and 5 captioned figures. Prepared for submission to The Open journal of Astrophysics
