Development of a Vertex Finding Algorithm using Recurrent Neural Network
This work addresses vertex reconstruction for high-energy physics experiments, specifically at future lepton colliders like the International Linear Collider, representing an incremental improvement over existing methods.
The researchers tackled vertex finding for future lepton colliders by developing a novel algorithm using a customized Recurrent Neural Network with attention and encoder-decoder structures, achieving performance comparable to the standard ILC reconstruction algorithm.
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.