AI 中文总结
研究旨在开发机器学习模型预测三体引力相互作用中双星形成。通过在三体散射实验数据集上训练XGBoost二元分类器，输入30个物理特征，模型性能出色且可解释，推理快，能为恒星系统大规模模拟提供有效概率估计。
AI 中文摘要
三体相互作用在恒星系统中频繁发生，本质上具有混沌性，用直接的N体积分法建模计算成本高。预测此类相互作用是否导致双星形成具有挑战性。本文旨在开发一个准确且具有物理可解释性的机器学习模型，从三体相互作用的初始条件预测双星形成，评估其可靠性并确定最能决定结果的物理参数。我们在使用REBOUND代码和IAS15积分器计算的三体散射实验平衡数据集上训练了一个XGBoost二元分类器。模型输入由30个描述初始构型质量、能量和运动学的物理特征组成。该分类器在平衡测试集上表现出色，各项指标均较高，特征重要性分析表明结果主要由相互作用的质量层次和硬度比决定。预测概率校准良好，推理速度比直接N体积分快约400倍，模型在不同相互作用半径上泛化良好。这些结果表明机器学习可为三体相互作用中双星形成提供快速、准确且具有物理可解释性的预测，对直接N体模拟是实用补充，可在恒星系统大规模模拟中进行有效概率估计。
英文摘要
Three-body encounters are frequent events in stellar systems, intrinsically chaotic, and computationally costly to model with direct N-body integration. Predicting whether such encounters lead to binary formation is therefore challenging, particularly in large-scale simulations. We aim to develop an accurate and physically interpretable machine-learning model that predicts binary formation from the initial conditions of three-body encounters, to assess its reliability, and to identify the physical parameters that most strongly determine the outcome. We trained an XGBoost binary classifier on a balanced dataset of three-body scattering experiments computed with the REBOUND code and the IAS15 integrator. The input to the model consists of 30 physically motivated features describing the masses, energies, and kinematics of the initial configuration. The classifier achieves excellent performance on a balanced test set, with accuracy, precision, recall, and F1-score all above 0.94, and with ROC-AUC and PR-AUC values of 0.99. Feature-importance analysis shows that the outcome is governed primarily by the mass hierarchy and hardness ratio of the encounter, followed by velocity fraction and mass entropy. The predicted probabilities are well calibrated, with an expected calibration error (ECE) of 0.02. Inference is approximately 400 times faster than direct N-body integration. The model also generalizes well across encounter radii, although its performance decreases in the weak-interaction regime where binary formation becomes intrinsically rare. These results show that machine learning can provide fast, accurate, and physically interpretable predictions of binary formation in three-body encounters. Such models offer a practical complement to direct N-body simulations and may enable efficient probability estimates in large-scale simulations of stellar systems.
Comments10 pages, 6 figures
