Visualization for physics analysis improvement and applications in BESIII

Zhi-Jun Li, Ming-Kuan Yuan, Yun-Xuan Song, Yan-Gu Li, Jing-Shu Li, Sheng-Sen Sun, Xiao-Long Wang, Zheng-Yun You, Ya-Jun Mao

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PDF(8688 KB)
Front. Phys. ›› 2024, Vol. 19 ›› Issue (6) : 64201. DOI: 10.1007/s11467-024-1422-7
RESEARCH ARTICLE

Visualization for physics analysis improvement and applications in BESIII

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Abstract

Modern particle physics experiments usually rely on highly complex and large-scale spectrometer devices. In high energy physics experiments, visualization helps detector design, data quality monitoring, offline data processing, and has great potential for improving physics analysis. In addition to the traditional physics data analysis based on statistical methods, visualization provides unique intuitive advantages in searching for rare signal events and reducing background noises. By applying the event display tool to several physics analyses in the BESIII experiment, we demonstrate that visualization can benefit potential physics discovery and improve the signal significance. With the development of modern visualization techniques, it is expected to play a more important role in future data processing and physics analysis of particle physics experiments.

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particle physics experiments / visualization / physics analysis / BESIII

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Zhi-Jun Li, Ming-Kuan Yuan, Yun-Xuan Song, Yan-Gu Li, Jing-Shu Li, Sheng-Sen Sun, Xiao-Long Wang, Zheng-Yun You, Ya-Jun Mao. Visualization for physics analysis improvement and applications in BESIII. Front. Phys., 2024, 19(6): 64201 https://doi.org/10.1007/s11467-024-1422-7

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Declarations

The authors declare that they have no competing interests and there are no conflicts.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 12175321, 12150005, 11975021, 11675275, and U1932101), the National Key Research and Development Program of China (Nos. 2023YFA1606000, 2020YFA0406300, and 2020YFA0406400), the State Key Laboratory of Nuclear Physics and Technology, Peking University (Nos. NPT2020KFY04 and NPT2020KFY05), Chinese Academy of Sciences (CAS) Large-Scale Scientific Facility Program, and National College Students Innovation and Entrepreneurship Training Program, Sun Yat-sen University.

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