基于等变图规范化流的星系合并重建
Galaxy Merger Reconstruction with Equivariant Graph Normalizing Flows
摘要：
现代天文学中一个尚未解决的关键问题是星系如何在$Λ$ CDM模型的范式下形成和演化。一个关键的限制因素是缺乏强有力的工具，无法通过统计模型来描述合并历史。在这项工作中，我们采用生成图网络，E（n）等变图规范化流模型。我们证明，通过将祖细胞作为一个图形，我们的模型鲁棒地恢复它们的分布，包括它们的质量，合并红移和成对距离在红移z=2的条件下，其z=0的属性。该模型的生成性质使其他下游任务成为可能，包括无可能性推理，检测异常和识别祖先特征的微妙相关性。
英文摘要：
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.
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