We use explainable neural networks to connect the evolutionary history of dark matter halos with their density profiles. The network captures independent factors of variation in the density profiles within a low-dimensional representation, which we physically interpret using mutual information. Without any prior knowledge of the halos' evolution, the network recovers the known relation between the early time assembly and the inner profile, and discovers that the profile beyond the virial radius is described by a single parameter capturing the most recent mass accretion rate. The results illustrate the potential for machine-assisted scientific discovery in complicated astrophysical datasets.

翻译：我们采用可解释神经网络，将暗物质晕的演化历史与其密度轮廓关联起来。该网络通过低维表征捕捉密度轮廓中的独立变化因子，并利用互信息对这些因子进行物理诠释。无需依赖晕演化的先验知识，网络不仅恢复了早期物质聚集与内部轮廓之间的已知关系，还发现维里半径外的轮廓可由一个参数描述，该参数捕获了最近的质量吸积率。这一结果展现了在复杂天体物理数据集中借助机器辅助进行科学发现的潜力。