论文标题
平均运动共振行为的机器学习预测 - 平面案例
Machine learning prediction for mean motion resonance behaviour -- The planar case
论文作者
论文摘要
最近，机器学习已被用来研究综合哈密顿系统的动态和混乱的三体问题。在这项工作中，我们考虑了不可综合系统中常规运动的中间情况：2：3中对象的行为与海王星平均运动共振。我们表明，鉴于最初的6250年数值集成的初始数据，最佳训练的人工神经网络（ANN）可以预测在随后的18750年演化中2：3谐振器的轨迹，涵盖了组合时间段的完整库周期。通过将ANN对谐振角度的预测与数值集成结果进行比较，前者可以以仅至几个度的准确性来预测谐振角度，同时它具有大大节省计算时间的优势。更具体地说，训练有素的ANN可以有效地测量2：3谐振器的谐振幅度，因此提供了一种可以识别谐振候选者的快速方法。这可能有助于对未来的调查中发现大量的KBO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.
