AI 中文总结
该研究针对星系团质量测量问题，提出基于GRU的深度学习框架，利用ICM径向剖面预测三维质量剖面，经模拟训练验证效果良好，还应用于实际观测，为星系团质量推断提供数据驱动框架，能弥合模拟与观测差距并扩展到多波长数据。
AI 中文摘要
准确的星系团质量测量对宇宙学至关重要，但传统的流体静力学平衡（HSE）方法可能存在系统偏差，特别是在动态扰动系统中。我们提出了一个基于门控循环单元（GRU）的深度学习框架，用于从球对称平均的星系团内介质（ICM）径向剖面预测星系团的三维质量剖面。通过将ICM剖面视为序列数据，GRU捕获径向依赖性并自然地处理具有不同径向采样的剖面。我们使用“三百计划”的高分辨率流体动力学模拟对模型进行训练和验证，在大多数星系团区域实现了无偏质量预测，典型的1σ散射约为5%，显著优于HSE估计。该模型提供了与半径相关的不确定性估计，并且对数据质量和星系团形态的变化具有鲁棒性。当在独立的模拟套件（GIZMO-SIMBA和GADGET-X）上联合训练时，它成功地在两个模拟中进行了泛化。特征重要性分析表明，封闭气体质量是主要预测因子，压力和温度提供了关于径向质量分布的额外信息。我们进一步将GRU模型应用于XMM-Newton对REXCESS和X-COP星系团样本的X射线观测，并将推断的质量剖面与HSE估计进行比较。对于高质量的X-COP样本，HSE质量系统地低于GRU预测，而REXCESS样本的质量差异平均接近零。这项工作提供了一个数据驱动的星系团质量推断框架，弥合了模拟和观测之间的差距，并且可以扩展到多波长数据集，包括Sunyaev-Zel'dovich和光学观测。
英文摘要
Accurate cluster mass measurements are crucial for cosmology, yet conventional hydrostatic equilibrium (HSE) methods can suffer from systematic biases, particularly in dynamically disturbed systems. We present a gated recurrent unit (GRU) based deep learning framework for predicting three-dimensional mass profiles of galaxy clusters from spherically averaged intra-cluster medium (ICM) radial profiles. By treating ICM profiles as sequential data, the GRU captures radial dependencies and naturally handles profiles with different radial samplings. We train and validate the model using high-resolution hydrodynamical simulations from The Three Hundred Project, achieving unbiased mass predictions with a typical 1$σ$ scatter of $\sim$5% over most of the cluster region, significantly improving upon HSE estimates. The model provides radius-dependent uncertainty estimates and remains robust against variations in data quality and cluster morphology. When trained jointly on independent simulation suites (GIZMO-SIMBA and GADGET-X), it successfully generalises across both simulations. Feature importance analysis shows that enclosed gas mass is the dominant predictor, with pressure and temperature providing additional information on the radial mass distribution. We further apply the GRU model to X-ray observations of the REXCESS and X-COP cluster samples from XMM-Newton and compare the inferred mass profiles with HSE estimates. The HSE masses are systematically lower than the GRU predictions for the higher-mass X-COP sample, while the REXCESS sample shows mass differences that are close to zero on average. This work provides a data-driven framework for cluster mass inference that bridges simulations and observations and can be extended to multi-wavelength datasets, including Sunyaev-Zel'dovich and optical observations.
Comments21 pages, 21 figures, 4 tables, submitted to A&A, abstract abridged for arXiv submission, comments welcome
