Improving the machine learning based vertex reconstruction for large liquid scintillator detectors with multiple types of PMTs

2022/05/09 by Zi-Yuan Li, Li, Zi-Yuan, Zhen Qian +15 · 2 citations
Medicine · Physics and Astronomy · #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Instrumentation and Detectors (physics.ins-det) #Medical Imaging Techniques and Applications #Nuclear Physics and Applications #Radiation Detection and Scintillator Technologies

paper · pdf · doi:10.48550/arxiv.2205.04039

openalex publication_date 2022/05/09 · openalex created_date 2022/05/22 · openalex updated_date 2026/07/28

Abstract

Precise vertex reconstruction is essential for large liquid scintillator detectors. A novel method based on machine learning has been successfully developed to reconstruct the event vertex in JUNO previously. In this paper, the performance of machine learning based vertex reconstruction is further improved by optimizing the input images of the neural networks. By separating the information of different types of PMTs as well as adding the information of the second hit of PMTs, the vertex resolution is improved by about 9.4 % at 1 MeV and 9.8 % at 11 MeV, respectively.

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• Unity based virtual reality for detector and event visualization in JUNO experiment
• Vertex reconstruction in the TAO experiment

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