arXiv 2007.037202020-07-07astro-ph.EPcs.LG

柯伊伯带种群的机器学习分类

Machine Learning Classification of Kuiper Belt Populations

University of Arizona（亚利桑那大学）

Rachel A. Smullen, Kathryn Volk

备注：: Accepted for publication in MNRAS. 13 pages, 14 figures. Companion notebooks and data for this work can be found at https://github.com/rsmullen/KBO_Classifier
DOI：: 10.1093/mnras/staa1935

摘要：

在太阳系的外围，柯伊伯带包含了由行星形成和迁移以及来自当今巨行星结构的引力扰动共同塑造的动态子群。将观测到的柯伊伯带天体（KBO）细分为不同的动力学类别是基于它们当前轨道演化的轨道数值积分。在这里，我们证明了机器学习算法是一种很有前途的工具，可以减少这种分类所需的计算时间和人力。使用梯度提升分类器（Gradient Boosting Classifier），这是一种机器学习回归树分类器，基于来自短期数值模拟的特征进行训练，我们将观察到的KBO分为四个广泛的，动态不同的群体-经典，共振，分离和散射-对于542个安全分类的KBO的测试集，准确率> 97%。超过80%的物体具有$>3σ$类成员概率，这表明机器学习方法是根据每个群体的基本动态特征进行分类的。我们还演示了如何，通过使用传统方法的计算节省，我们可以快速获得类成员的分布，通过检查从观测误差中提取的对象克隆的集合。我们发现错误分类的两个主要原因：物体轨道的固有模糊性-例如，处于共振边缘的物体-以及训练集中缺乏代表性的例子。这项工作提供了一个有希望的途径，探索快速和准确的分类数以千计的新的KBO预计将在未来十年的调查发现。

英文摘要：

In the outer solar system, the Kuiper Belt contains dynamical sub-populations sculpted by a combination of planet formation and migration and gravitational perturbations from the present-day giant planet configuration. The subdivision of observed Kuiper Belt objects (KBOs) into different dynamical classes is based on their current orbital evolution in numerical integrations of their orbits. Here we demonstrate that machine learning algorithms are a promising tool for reducing both the computational time and human effort required for this classification. Using a Gradient Boosting Classifier, a type of machine learning regression tree classifier trained on features derived from short numerical simulations, we sort observed KBOs into four broad, dynamically distinct populations - classical, resonant, detached, and scattering - with a >97 per cent accuracy for the testing set of 542 securely classified KBOs. Over 80 per cent of these objects have a $>3σ$ probability of class membership, indicating that the machine learning method is classifying based on the fundamental dynamical features of each population. We also demonstrate how, by using computational savings over traditional methods, we can quickly derive a distribution of class membership by examining an ensemble of object clones drawn from the observational errors. We find two major reasons for misclassification: inherent ambiguity in the orbit of the object - for instance, an object that is on the edge of resonance - and a lack of representative examples in the training set. This work provides a promising avenue to explore for fast and accurate classification of the thousands of new KBOs expected to be found by surveys in the coming decade.

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