Development of a vertex finding algorithm using Recurrent Neural Network

• DOI: 10.1016/j.nima.2022.167836
• Publication Date: 2022-11-24T10:58:56Z
• Authors: Kiichi Goto; Taikan Suehara; Tamaki Yoshioka; Masakazu Kurata; Hajime Nagahara; Yuta Nakashima; Noriko Takemura; Masako Iwasaki
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Physics - Instrumentation and Detectors; FOS: Physical sciences; Instrumentation and Detectors (physics.ins-det); 02 engineering and technology; High Energy Physics - Experiment; Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex); Physics - Data Analysis, Statistics and Probability; 0202 electrical engineering, electronic engineering, information engineering; Data Analysis, Statistics and Probability (physics.data-an)

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.<br/>16 pages, 9 figures<br/>