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.

中文翻译：

---

柯伊伯带种群的机器学习分类


在太阳系外层，柯伊伯带包含由行星形成、迁移以及当今巨行星结构的引力摄动共同塑造的动态亚群。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其当前轨道数值积分中的轨道演化。在这里，我们证明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（一种机器学习回归树分类器，根据短数值模拟得出的特征进行训练），我们将观察到的 KBO 分类为四个广泛的、动态不同的群体——经典、共振、分离和散射——>97% 542 个安全分类 KBO 测试集的准确性。这些对象中超过 80% 的类成员概率为 > 3σ，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节省的计算量，通过检查从观察误差中提取的对象克隆集合来快速得出类成员资格的分布。我们发现错误分类的两个主要原因：物体轨道固有的模糊性（例如，处于共振边缘的物体）以及训练集中缺乏代表性示例。这项工作为探索对未来十年预计将通过调查发现的数千个新柯伊伯带天体进行快速、准确的分类提供了一条有前途的途径。