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arXiv · 2101.11906

Development of a Vertex Finding Algorithm using Recurrent Neural Network

Kiichi Goto·Taikan Suehara·Tamaki Yoshioka·Masakazu Kurata·Hajime Nagahara·Yuta Nakashima·Noriko Takemura·Masako Iwasaki
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Abstract

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.

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Kiichi Goto, Taikan Suehara, Tamaki Yoshioka, Masakazu Kurata, Hajime Nagahara, Yuta Nakashima, Noriko Takemura, Masako Iwasaki. 2021-01-28. Development of a Vertex Finding Algorithm using Recurrent Neural Network. https://doi.org/10.1016/j.nima.2022.167836

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