AI 中文总结
研究旨在构建用于双星-单星散射最终状态的代理模型，通过在数值积分实验数据集上训练XGBoost多类分类器，该模型准确率高，能预测最终状态，且残余误差源于混沌区域，体现了机器学习在处理此类混沌问题上的有效性。
AI 中文摘要
双星-单星相遇在恒星系统中频繁发生，构成了一个不可积的混沌三体问题，用N体代码处理时计算成本很高。我们旨在构建一个准确且具有物理可解释性的代理模型，用于预测双星-单星散射的最终状态（电离、飞越、交换或层级），评估其概率预测的可靠性，并将预测失败追溯到散射问题的潜在混沌性质。我们在一个经过数值积分的双星-单星散射实验的类平衡数据集上训练了一个XGBoost多类分类器，使用了具有物理动机的特征。该模型的测试准确率为88.32%，前两名准确率为98.77%。特征重要性表明双星硬度是主要预测因素。错误分类集中在区分交换和层级结果的混沌边界附近。盆地熵分析表明，这种错误分类是由于参数空间中固有的模糊区域。因此，一个快速的机器学习代理可以高精度地预测双星-单星散射的最终状态，残余误差来自混沌区域而非模型不足。
英文摘要
Context. Binary-single encounters are particularly frequent in dense stellar environments, where they play a central role in shaping the dynamical evolution of their host systems. However, predicting their final outcomes remains an open question due to the intrinsic chaotic nature of the three-body problem. This challenge motivates the adoption of data-driven machine learning (ML) methods. Aims. We investigate whether ML can predict the final outcomes of binary-single encounters from initial conditions alone. Methods. We generated 5.8 million binary-single scattering simulations using the REBOUND N-body package with the IAS15 integrator. A cascaded binary classification strategy, comprising four sequential XGBoost classifiers, and a single multi-class model were trained on the synthetic dataset and compared. Results. The cascaded strategy outperforms the single multi-class model across all metrics. F1-scores for the cascaded models exceed 0.92, with precision-recall area under the curve (PR-AUC) values reaching 0.99, compared to 0.95 for the multi-class model. Feature importance analysis identifies encounter timescale, binary hardness, and mass ratio as key predictors. Misclassification analysis shows that prediction failures concentrate near chaotic boundaries where the outcome is sensitive to small perturbations. Speed benchmarks demonstrate that the cascaded models are up to 300 times faster than direct N-body integrations. However, all models fail to generalize to new datasets, highlighting a key limitation. Conclusions. This study demonstrates that, for any specific environment and data distribution, the proposed cascaded ML strategy provides a robust and rapid framework for predicting binary-single scattering outcomes.
Comments9 pages, 7 figures, Submitted to A&A
