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Development of a Vertex Finding Algorithm using Recurrent Neural Network

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arxiv 2101.11906 v5 pith:V6PUUXXO submitted 2021-01-28 physics.data-an cs.LGhep-exphysics.ins-det

Kiichi Goto , Taikan Suehara , Tamaki Yoshioka , Masakazu Kurata , Hajime Nagahara , Yuta Nakashima , Noriko Takemura , Masako Iwasaki This is my paper · ORCID
classification physics.data-ancs.LGhep-exphysics.ins-det
keywords vertexalgorithmcolliderfindingnetworkneuralrecurrentseeds
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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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