AI 中文总结
综述海王星外天体稳定性分析，结合经典与现代方法绘制相空间、分类轨道传输，机器学习作补充，指出基于哈密顿动力学的混合动态 - 统计框架是探索高维参数空间的最有前景方向。
AI 中文摘要
海王星外区域（30 - 50天文单位）是一个动态结构化的冰质小行星库，其轨道结构反映了共振动力学、混沌传输和巨行星的长期引力塑造。本综述综合了海王星外天体（TNOs）动力学研究的最新进展，重点关注平均运动和长期共振以及混沌扩散。经典指标与现代方法相结合用于绘制TNO相空间，异常扩散框架可对轨道传输分类，机器学习成为传统动力学方法的有力补充。最有前景的方向是基于哈密顿动力学的混合动态 - 统计框架。
英文摘要
The trans-Neptunian region (30-50 AU) is a dynamically structured reservoir of icy planetesimals whose orbital architecture reflects resonant dynamics, chaotic transport, and long-term gravitational sculpting by the giant planets. This review synthesizes recent developments in the dynamical investigation of trans-Neptunian objects (TNOs), with an emphasis on mean-motion and secular resonances, as well as chaotic diffusion, in a system whose growing observational census makes it an ideal testbed for chaos detection methods. Classical indicators, including Lyapunov exponents, MEGNO, SALI/GALI, and frequency map analysis, provide the quantitative backbone for mapping TNO phase space and are complemented by modern approaches such as Lagrangian descriptors, the FAIR resonance identification method, entropy-based chaos indicators, and recurrence plot divergence methods. An anomalous diffusion framework, in which mean squared displacement scales as a power law in time, further enables classification of sub- and superdiffusive orbital transport. Machine learning has emerged as a powerful complement to traditional dynamical methods: surrogate classifiers, deep neural network solvers, and hybrid physics-data-driven frameworks together extend reliable prediction horizons in chaotic regimes and open new routes for Bayesian inference of migration scenarios. The review concludes that the most promising path forward lies in hybrid dynamical-statistical frameworks anchored to Hamiltonian dynamics, enabling efficient exploration of high-dimensional parameter spaces informed by the expanding body of trans-Neptunian observations.
Commentsreview manuscript, 35 pages, 13 figures
