Development of a Vertex Finding Algorithm using Recurrent Neural Network
| Autor: | Kiichi Goto, Taikan Suehara, Tamaki Yoshioka, Masakazu Kurata, Hajime Nagahara, Yuta Nakashima, Noriko Takemura, Masako Iwasaki |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Nuclear and High Energy Physics Computer Science - Machine Learning Physics - Instrumentation and Detectors Physics::Instrumentation and Detectors FOS: Physical sciences Instrumentation and Detectors (physics.ins-det) High Energy Physics - Experiment Machine Learning (cs.LG) High Energy Physics - Experiment (hep-ex) Physics - Data Analysis Statistics and Probability Physics::Accelerator Physics Instrumentation Data Analysis Statistics and Probability (physics.data-an) ComputingMethodologies_COMPUTERGRAPHICS |
| DOI: | 10.48550/arxiv.2101.11906 |
| Popis: | 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. Comment: 16 pages, 9 figures |
| Databáze: | OpenAIRE |
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