发表机构
Indian Institute of Technology Patna（印度巴特那理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出图注意力网络GAMANet，基于GAMA DR4数据同时预测星系与暗物质晕属性，在测试集上取得高精度，证明图学习能有效利用观测数据与环境关系。
AI 中文摘要
我们开发了一种基于图的深度学习模型GAMANet，利用GAMA DR4数据集预测星系和暗物质晕属性。我们结合多个星表构建了一个包含19,469个星系的统一样本，并推导出一组光度、光谱、形态和环境特征。星系样本被表示为一个图，其中星系作为节点，基于空间邻近性和群组成员关系的物理动机连接作为边。然后使用带有GATv2层的图注意力网络从星系邻域中学习信息，同时为邻近星系分配不同的权重。该模型同时预测暗物质晕质量、恒星质量以及一个特定的恒星形成率代理量。在测试集上，GAMANet在暗物质晕质量、恒星质量和SSFR代理量上分别达到了$R^2 = 0.958$、$0.985$和$0.982$，暗物质晕质量的RMSE为$0.237$ dex。这些结果表明，基于图的学习可以直接从观测数据中提取有关星系和暗物质晕属性的有用信息，同时纳入星系图中编码的环境关系。
英文摘要
We develop a graph-based deep learning model, GAMANet, to predict galaxy and halo properties using the GAMA DR4 dataset. Multiple catalogues are combined to construct a unified sample of 19,469 galaxies and derive a set of photometric, spectroscopic, morphological, and environmental features. The galaxy sample is represented as a graph, with galaxies as nodes and physically motivated connections based on spatial proximity and group membership as edges. A Graph Attention Network with GATv2 layers is then used to learn information from the galaxy neighbourhood while assigning different weights to neighbouring galaxies. The model simultaneously predicts halo mass, stellar mass, and a specific star formation rate proxy. On the test set, GAMANet achieves $R^2 = 0.958$, $0.985$, and $0.982$ for halo mass, stellar mass, and SSFR proxy, respectively, with an RMSE of $0.237$ dex for halo mass. These results demonstrate that graph-based learning can extract useful information about galaxy and halo properties directly from observational data while incorporating the environmental relationships encoded in the galaxy graph.
Comments23 pages, 6 figures
