发表机构
University of Virginia; Massachusetts Institute of Technology; New York University; Flatiron Institute（弗吉尼亚大学; 麻省理工学院; 纽约大学; 平顿研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种条件生成模型框架，通过随机插值方法从恒星密度映射银河系质量星系的暗物质轮廓，精度约0.1 dex，并具备不确定性估计，为利用成像巡天数据重建暗物质分布提供了新途径。
AI 中文摘要
星系中的暗物质晕轮廓蕴含着关于暗物质本质和星系形成的关键信息。约束银河系以外星系的暗物质轮廓传统上需要昂贵的光谱观测以获取运动学信息。在本文中，我们探索了一种条件生成模型框架，用以从恒星密度轮廓映射银河系质量星系的暗物质轮廓，而恒星密度轮廓可通过大规模光度成像巡天更易获得。作为概念验证，我们训练模型学习DREAMS流体动力学模拟套件中无仪器系统效应的重子恒星分布与底层暗物质密度图之间的随机桥接。我们恢复的二维暗物质密度轮廓的典型精度约为0.1 dex（对数尺度下约为真实值的1.5%）。随机采样过程提供了预测暗物质图的不确定性估计，典型值约为0.1 dex。使用来自IllustrisTNG和FIRE模拟的银河系质量星系进行的域外测试表明，虽然模型可以定性地推广到IllustrisTNG的TNG50星系，但模型对星系形成模型敏感，对FIRE测试星系的内轮廓（r≲5 kpc）存在约0.2-0.4 dex的高估。未来工作将探索使用额外的模拟套件进行训练和/或基于额外信息（如多波段图像）进行条件约束。我们的结果是朝着使用生成模型作为灵活、具有不确定性意识的框架，将来自大型成像巡天的未来数据转化为空间分辨的暗物质图迈出的第一步。
英文摘要
The dark matter halo profile in galaxies holds key information about the nature of dark matter and galaxy formation. Constraining the dark matter profile of galaxies beyond the Milky Way traditionally requires expensive spectroscopic observations for kinematic information. In this paper, we explore a conditional generative model framework to map the dark matter profile of Milky Way-mass galaxies from stellar density profiles, more easily obtainable through large photometric imaging surveys. As a proof of concept, we train the model to learn a stochastic bridge between instrument systematics-free baryonic stellar distributions and underlying dark matter density maps from the DREAMS hydrodynamics simulation suite. We recover 2D dark matter density profiles with a typical accuracy of $\sim0.1$ dex ($\sim1.5$% of the truth in log scale). The stochastic sampling procedure provides uncertainty estimates of the predicted dark matter map, with typical values $\sim0.1$ dex. Out-of-domain tests with Milky Way-mass galaxies from IllustrisTNG and FIRE simulations show that, while the model can qualitatively be generalized to TNG50 galaxies from IllustrisTNG, the model is sensitive to the galaxy formation model, with $\sim0.2$-$0.4$ dex over-prediction for the inner profiles ($r\lesssim5$ kpc) of the FIRE test galaxies. Future work will explore training with additional suites of simulations and/or conditioning on additional information, such as multi-band images. Our results are a first step towards using generative models as a flexible, uncertainty-aware framework for turning forthcoming data from large imaging surveys into spatially resolved dark matter maps.
Comments18 pages plus appendices, 10 figures. Comments are welcome. The code accompanying this paper, for training the model and reproducing the analysis, is available in a GitHub repository (https://github.com/xou-mit/star2dm)
