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Method for detector description transformation to Unity and application in BESIII

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arxiv 2206.10117 v2 pith:FR5MUW74 submitted 2022-06-21 physics.ins-det

Method for detector description transformation to Unity and application in BESIII

Kai-Xuan Huang , Zhi-Jun Li , Zhen Qian , Jiang Zhu , Hao-Yuan Li , Yu-Mei Zhang , Sheng-Sen Sun , Zheng-Yun You This is my paper
classification physics.ins-det
keywords detectorexperimentsmethodsoftwareunityvisualizationapplicationsbesiii
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Detector and event visualization are essential parts of the software used in high-energy physics (HEP) experiments. Modern visualization techniques and multimedia production platforms such as Unity provide impressive display effects and professional extensions for visualization in HEP experiments. In this study, a method for automatic detector description transformation is presented, which can convert the complicated HEP detector geometry from GDML in offline software to 3D modeling in Unity. The method was successfully applied in the BESIII experiment and can be further developed into applications such as event displays, data monitoring, or virtual reality. It has great potential in detector design, offline software development, physics analysis, and outreach for next-generation HEP experiments as well as applications in nuclear techniques for the industry.

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Cited by 2 Pith papers

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  1. Measurement of Born cross sections for $e^+e^-\to p\bar p$ at $\sqrt{s} =3.510-4.946$ GeV

    hep-ex 2026-06 unverdicted novelty 7.0

    BESIII reports Born cross sections for e+e- to p pbar at 47 energies between 3.51 and 4.95 GeV and determines |GE/GM| and |GM| for the first time from angular distributions at large timelike momentum transfer.

  2. Learning transferable event representations for charmed baryon physics at BESIII

    physics.data-an 2026-07 conditional novelty 6.0

    A Particle Transformer pre-trained on simulated Lambda_c events transfers across 12 decay channels, improving classification and momentum-direction regression over training from scratch in low-statistics regimes.