Deep learning is a rapidly-evolving technology with possibility to significantly improve physics reach of collider experiments. In this study we developed a novel algorithm of vertex finding for future lepton colliders such as the International Linear Collider. We deploy two networks; one is simple fully-connected layers to look for vertex seeds from track pairs, and the other is a customized Recurrent Neural Network with an attention mechanism and an encoder-decoder structure to associate tracks to the vertex seeds. The performance of the vertex finder is compared with the standard ILC reconstruction algorithm.

翻译：深层学习是一种迅速发展的技术,有可能大大改善对撞实验的物理范围。在这项研究中,我们开发了一种新颖的顶点搜索算法,用于未来的列子对撞机,如国际线性对撞机。我们部署了两个网络;一个是简单的全连接层,以寻找轨道对子的顶点种子,另一个是定制的具有关注机制的经常性神经网络,一个编码器解码器结构,将轨道与顶点种子联系起来。顶点发现器的性能与标准的国际法委员会重建算法相比。