Machine learning prediction for mean motion resonance behaviour - The planar case
Machine learning prediction for mean motion resonance behaviour - The planar case
复制标题
平均运动共振行为的机器学习预测 - 平面情况
DOI：
10.1093/mnras/stac166
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
N. Georgakarakos
中科院分区：
文献类型：
--
作者：
Xin Li;Jian Li;Zhihong Xia;N. Georgakarakos
最近，机器学习已被用于研究可积哈密顿系统的动力学和混沌三体问题。在这项工作中，我们考虑了一种非积分系统中规则运动的中间情况：与海王星的2:3平均运动共振中物体的行为。我们表明，给定6250年数值积分的初始数据，训练最好的人工神经网络（ANN）可以预测在随后的18750年演变中2:3谐振器的轨迹，覆盖整个组合时间周期的整个振动周期。将我们的人工神经网络对共振角的预测结果与数值积分结果进行比较，前者预测共振角的精度仅为几度，而其优点是大大节省了计算时间。更具体地说，训练后的人工神经网络可以有效地测量2:3谐振器的谐振幅度，从而提供了一种快速识别候选谐振器的方法。这可能有助于对未来调查中发现的大量柯伊伯带天体进行分类。
Most recently, machine learning has been used to study the dynamics of integrable Hamiltonian systems and the chaotic 3-body problem. In this work, we consider an intermediate case of regular motion in a non-integrable system: the behaviour of objects in the 2:3 mean motion resonance with Neptune. We show that, given initial data from a short 6250 yr numerical integration, the best-trained artificial neural network (ANN) can predict the trajectories of the 2:3 resonators over the subsequent 18750 yr evolution, covering a full libration cycle over the combined time period. By comparing our ANN's prediction of the resonant angle to the outcome of numerical integrations, the former can predict the resonant angle with an accuracy as small as of a few degrees only, while it has the advantage of considerably saving computational time. More specifically, the trained ANN can effectively measure the resonant amplitudes of the 2:3 resonators, and thus provides a fast approach that can identify the resonant candidates. This may be helpful in classifying a huge population of KBOs to be discovered in future surveys.
“与组方案相关的角色产品和平衡集”（预印本）。
DOI：
--
发表时间：
--
期刊：
影响因子：
--
作者：
通讯作者：
--