论文标题
星系合并重建与均衡图的标准化流量
Galaxy Merger Reconstruction with Equivariant Graph Normalizing Flows
论文作者
论文摘要
现代天文学中的一个关键但尚未解决的问题是，在$λ$ CDM型号的范式下形成和进化的星系如何形成和进化。一个关键的限制因素在于缺乏通过统计模型来描述合并历史的强大工具。在这项工作中，我们采用生成图网络E（n）eprovariant图归一化流量模型。我们证明，通过将祖细胞视为图形，我们的模型可稳健地恢复它们的分布，包括其质量，合并红移和红移z = 2的成对距离，该距离在其Z = 0属性上进行条件。该模型的生成性质可以实现其他下游任务，包括无可能的推理，检测异常并识别祖细胞特征的微妙相关性。
A key yet unresolved question in modern-day astronomy is how galaxies formed and evolved under the paradigm of the $Λ$CDM model. A critical limiting factor lies in the lack of robust tools to describe the merger history through a statistical model. In this work, we employ a generative graph network, E(n) Equivariant Graph Normalizing Flows Model. We demonstrate that, by treating the progenitors as a graph, our model robustly recovers their distributions, including their masses, merging redshifts and pairwise distances at redshift z=2 conditioned on their z=0 properties. The generative nature of the model enables other downstream tasks, including likelihood-free inference, detecting anomalies and identifying subtle correlations of progenitor features.
