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.
@article{arxiv.2101.11906,
title = {Development of a Vertex Finding Algorithm using Recurrent Neural Network},
author = {Kiichi Goto and Taikan Suehara and Tamaki Yoshioka and Masakazu Kurata and Hajime Nagahara and Yuta Nakashima and Noriko Takemura and Masako Iwasaki},
journal= {arXiv preprint arXiv:2101.11906},
year = {2023}
}