本文首次提出基于 U-Net 的深度学习方法，从多波段模拟观测中直接推断星系团投影总质量密度图，在 The Three Hundred 项目数据上验证了质量估计偏差约 1%、散射约 3%，为重建星系团物质分布提供了可补充引力透镜的新途径。
摘要：
星系团作为宇宙中质量最大的引力束缚天体，主要由暗物质主导，但遗憾的是，暗物质只能通过其与发光重子的相互作用来研究，且需要借助一些简化假设，这些假设会引入不期望的偏差。在这项工作中，我们首次提出了一种基于 U-Net 架构的深度学习方法，从多波段理想化的模拟星系团观测中直接推断投影总质量密度图。该模型使用来自 The Three Hundred 项目模拟星系团的大量模拟图像数据集进行训练。尽管机器学习（ML）模型不依赖于星系团内介质动力学的假设，但我们的整个方法依赖于流体动力学模拟中所实现的物理选择，这是该方法的一个局限性。通过不同的指标来评估所推断密度图的保真度，我们表明预测的总质量分布与真实的模拟星系团非常吻合。因此，看到积分晕质量几乎无偏也就不足为奇了，多视角的最佳结果偏差约为 1%，散射也非常小，基本在 3% 以内。这一结果表明，这种 ML 方法为重建星系团中的整体物质分布提供了一种替代且更易获取的途径，可以补充引力透镜方法。
英文摘要：
A galaxy cluster as the most massive gravitationally-bound object in the Universe, is dominated by Dark Matter, which unfortunately can only be investigated through its interaction with the luminous baryons with some simplified assumptions that introduce an un-preferred bias. In this work, we, {\it for the first time}, propose a deep learning method based on the U-Net architecture, to directly infer the projected total mass density map from idealised observations of simulated galaxy clusters at multi-wavelengths. The model is trained with a large dataset of simulated images from clusters of {\sc The Three Hundred Project}. Although Machine Learning (ML) models do not depend on the assumptions of the dynamics of the intra-cluster medium, our whole method relies on the choice of the physics implemented in the hydrodynamic simulations, which is a limitation of the method. Through different metrics to assess the fidelity of the inferred density map, we show that the predicted total mass distribution is in very good agreement with the true simulated cluster. Therefore, it is not surprising to see the integrated halo mass is almost unbiased, around 1 per cent for the best result from multiview, and the scatter is also very small, basically within 3 per cent. This result suggests that this ML method provides an alternative and more accessible approach to reconstructing the overall matter distribution in galaxy clusters, which can complement the lensing method.
The Three Hundred project: Low Gas Fraction Galaxy Clusters properties and their environment
Sapienza Università di Roma（罗马第一大学）；INAF-Osservatorio Astronomico di Roma（罗马天文台）；Universidad Autónoma de Madrid（马德里自治大学）；Università degli Studi di Napoli "Federico II"（那不勒斯费德里科二世大学）；Université Paris-Saclay（巴黎萨克雷大学）；INAF–Osservatorio Astronomico di Brera（布雷拉天文台）
The Three Hundred project: cosmic web identification from 2D gas and Compton-$y$ maps of galaxy clusters outskirts
Sapienza Università di Roma（罗马第一大学）；Universidad Autónoma de Madrid（马德里自治大学）；Centro de Investigación Avanzada en Física Fundamental (CIAFF)（基础物理高级研究中心）；University of Edinburgh（爱丁堡大学）；Universidad Rey Juan Carlos（雷乌斯·胡安·卡洛斯国王大学）；Università degli Studi di Napoli Federico II（那不勒斯费德里科二世大学）
