标题： 从光到暗（DfL）：利用机器学习从发光示踪物推断晕属性，训练于宇宙学模拟。 一. 方法、概念验证与初步测试 显示英文标题

标题： Dark from light (DfL): Inferring halo properties from luminous tracers with machine learning trained on cosmological simulations. I. Method, proof of concept & preliminary testing

摘要： 我们提出从光到暗（DfL）——一种新的方法，用于在大范围星系巡天中推断暗区，利用基于当代宇宙学模拟训练的机器学习方法。 该算法的目的是为在大范围光谱星系巡天中估计暗物质晕质量及群组成员提供一种快速、直接且准确的途径。 这种方法需要极少量的输入参数，并能为输出的晕质量提供完整的概率分布函数。 为了实现这一点，我们在z=0-3的IllustrisTNG和EAGLE模拟上训练一系列随机森林（RF）回归模型，这些模型提供了从发光示踪物到暗物质晕属性的模型依赖性映射。 我们将各个回归模型整合到一个维里群发现算法（DfL）中，该算法对类似观测的数据输出晕属性。 我们在z=0-2范围内对EAGLE和IllustrisTNG模型以及交叉验证模式下测试了该方法。 我们证明，已知的晕质量可以以$\langle b \rangle = \pm 0.10\,$dex 的平均系统偏差（来源于模拟选择）恢复，各时期平均统计不确定性为$\langle \sigma \rangle = 0.12 \,$dex，并且中心-（核心）卫星分类准确率为96%。 我们确定，这种方法在晕质量恢复方面优于应用于通过朋友-朋友算法识别的群组的标准丰度匹配。 此外，我们将DfL的输出与强引力透镜在$z \sim 0$处的$M_* - M_{\rm Halo}$关系的观测约束进行比较，展示了这一新方法的前景。 最后，我们系统地量化了DfL在具有不同恒星质量不确定性和光谱不完全性的类似观测数据上的表现，从而实现了稳健的误差校准。 显示英文摘要

摘要： We present Dark from Light (DfL) - a novel method to infer the dark sector in wide-field galaxy surveys, leveraging a machine learning approach trained on contemporary cosmological simulations. The aim of this algorithm is to provide a fast, straightforward, and accurate route to estimating dark matter halo masses and group membership in wide-field spectroscopic galaxy surveys. This approach requires a highly limited number of input parameters and yields full probability distribution functions for the output halo masses. To achieve this, we train a series of Random Forest (RF) regression models on the IllustrisTNG and EAGLE simulations at z=0-3, which provide model-dependent mappings from luminous tracers to dark matter halo properties. We incorporate the individual regression models into a virial group-finding algorithm (DfL), which outputs halo properties for observational-like input data. We test the method at z=0-2 for both the EAGLE and IllustrisTNG models, as well as in a cross-validation mode. We demonstrate that known halo masses can be recovered with a mean systematic bias of $\langle b \rangle = \pm 0.10\,$dex (resulting from simulation choice), a mean statistical uncertainty of $\langle \sigma \rangle = 0.12 \,$dex across epochs, and a central - (core) satellite classification accuracy of 96%. We establish that this approach yields superior halo mass recovery to standard abundance matching applied to groups identified through a friends-of-friends algorithm. Additionally, we compare the outputs of DfL to observational constraints on the $M_* - M_{\rm Halo}$ relation from strong gravitational lensing at $z \sim 0$, demonstrating the promise of this novel approach. Finally, we systematically quantify how DfL performs on observational-like input data with varying stellar mass uncertainty and spectroscopic incompleteness, enabling robust error calibration.