MHD湍流中动力学状态的形态学识别：ScaleAware-JEPA
Morphological Identification of Dynamical Regimes in MHD Turbulence with ScaleAware-JEPA
浏览论文内容
中文总结 AI 辅助
本研究利用ScaleAware-JEPA从密度形态学习多尺度表示，发现其能比密度振幅更有序地组织MHD湍流的动力学状态，为湍流星际气体的动力学诊断提供了新坐标。
中文摘要 AI 辅助
湍流星际气体的形态反映了湍流、磁场和自引力的耦合作用，但仅凭密度振幅无法唯一确定动力学状态。我们使用ScaleAware-JEPA从密度数据中学习多尺度结构坐标，并测试这些学习到的坐标是否能比基于密度的条件更清晰地组织动力学状态。类团块、类纤维和类弥散的潜在邻域在湍流速度标度上表现出有序的递进，而这种递进是密度选择的群体无法重现的。训练过程中速度场和磁场被保留，训练后用作物理诊断：MHD状态占据学习表示中有序但重叠的区域，无量纲动力学平衡在整个潜在图谱上连贯变化。这些结果表明，从密度形态学学习到的表示可以获得一种由已建立的MHD诊断系统组织的几何结构。因此，多尺度形态学为超越密度振幅的动力学状态提供了有用的坐标。
英文摘要
The morphology of turbulent interstellar gas reflects the coupled action of turbulence, magnetic fields, and self-gravity, but density amplitude alone does not uniquely specify dynamical state. We use ScaleAware-JEPA to learn multiscale structural coordinates from density alone and test whether these learned coordinates organize dynamical states more clearly than density-based conditioning. Clump-like, filament-like, and diffuse-like latent neighborhoods show an ordered progression in turbulent velocity scaling that is not reproduced by density-selected populations. Velocity and magnetic fields are withheld during training and used afterward as post-training physical diagnostics: MHD states occupy ordered but overlapping regions of the learned representation, and dimensionless dynamical balances vary coherently across the full latent atlas. These results show that a representation learned from density morphology can acquire a geometry that is systematically organized by established MHD diagnostics. Multiscale morphology therefore provides a useful coordinate for dynamical state beyond density amplitude alone.
发表机构
- School of Astronomy and Space Science, Nanjing University（南京大学天文与空间科学学院）
- Key Laboratory of Modern Astronomy and Astrophysics (Nanjing University), Ministry of Education（教育部现代天文与天体物理重点实验室（南京大学））
- South-Western Institute for Astronomy Research, Yunnan University（云南大学西南天文研究所）
机构由 AI 辅助整理，请以论文原文为准。
