论文标题
通过卷积神经网络预测分层三重系统的稳定性
Predicting the Stability of Hierarchical Triple Systems with Convolutional Neural Networks
论文作者
论文摘要
由于其固有的混乱性，因此了解层次三重系统的长期演变是具有挑战性的，并且需要计算昂贵的模拟。在这里，我们提出了一个卷积神经网络模型，以通过在第一个$ 5 \ times 10^5 $内部二进制轨道上查看其演变来预测层次三元组的稳定性。我们采用正规化的几体代码海啸来模拟$ 5 \ times 10^6 $层次结构三元组，从中我们生成了大型培训和测试数据集。我们开发了十二种不同的网络配置，它们使用三元组的轨道元素的不同组合并比较其性能。我们的最佳模型使用6个时间序列，即半轴轴比率，内部和外偏心，相互倾斜度和围角的参数。该模型在曲线下达到了超过$ 95 \％$的区域，并告知了研究三重系统稳定性的相关参数。所有训练有素的模型均可公开使用，可以预测分层三重系统的稳定性$ 200 $ $ 200 $倍，比纯$ n $ body方法快。
Understanding the long-term evolution of hierarchical triple systems is challenging due to its inherent chaotic nature, and it requires computationally expensive simulations. Here we propose a convolutional neural network model to predict the stability of hierarchical triples by looking at their evolution during the first $5 \times 10^5$ inner binary orbits. We employ the regularized few-body code TSUNAMI to simulate $5\times 10^6$ hierarchical triples, from which we generate a large training and test dataset. We develop twelve different network configurations that use different combinations of the triples' orbital elements and compare their performances. Our best model uses 6 time-series, namely, the semimajor axes ratio, the inner and outer eccentricities, the mutual inclination and the arguments of pericenter. This model achieves an area under the curve of over $95\%$ and informs of the relevant parameters to study triple systems stability. All trained models are made publicly available, allowing to predict the stability of hierarchical triple systems $200$ times faster than pure $N$-body methods.
