Editorial17 Aug 2025
Bridging nuclear physics across energy scales: from neutrinoless double-beta decay to high-energy heavy-ion collisions
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Research article10 Apr 2026
An event visualization software based on Phoenix for the CEPC experiment
In high-energy physics (HEP) experiments, visualization software plays a pivotal role in detector design, offline software development, and event data analysis. The visualization tools integrate detailed detector geometries with complex event data models, providing researchers with invaluable insights into experimental results. Phoenix is an emerging general-purpose visualization platform for current and next-generation HEP experiments. In this study, we developed an event display software based on Phoenix for the CEPC experiment. It offers the necessary functionalities for visualizing detector geometries and displaying event data, allowing researchers to optimize detector design, test simulation and reconstruction algorithms, and analyze event data in a visualized manner. Additionally, we discuss the future applications of the event display software, including its use in online monitoring and the potential to build virtual reality projects for enhanced data visualization.
Zheng-Yun You, Yu-Mei Zhang, Tian-Zi Song, Xue-Sen Wang, Yu-Jie Zeng, Tao Lin
Research article10 Apr 2026
A neural network approach for two-body systems with spin and isospin degrees of freedom
We propose an enhanced machine learning method to calculate the ground state of two-body systems. Compared to the original method [Phys. Rev. Research 5, 033189 (2023)], the present method enables consideration of the spin and isospin degrees of freedom by employing a non-fully-connected deep neural network and unsupervised machine learning technique. The validity of this method is verified by calculating the unique bound state of the deuteron.
Jian Li, Hao-Zhao Liang, Chuan-Xin Wang, Tomoya Naito
Research article10 Apr 2026
Study of high-energy neutron-induced fission cross sections using Bayesian methods
High-energy neutron-induced fission data for actinide nuclides are vital role in the foundation for designing advanced nuclear energy systems, such as accelerator-driven subcritical systems and fast neutron reactors. In this study, the INCL++ code was used to calculate neutron-induced fission cross sections in the energy range of 100 MeV to 1.2 GeV. Bayesian optimization was employed to refine the parameters in the ABLA++ and GEMINI++ codes, ensuring closer agreement between the computational results and experimental data. We trained a Bayesian neural network using neutron-induced fission data and systematically compared the extrapolations with both theoretical calculations and experimental measurements. The results show that the Bayesian optimization method effectively reduces the chi-squared statistic between the theoretical predictions and the experimental data. Additionally, the Bayesian neural network demonstrates the ability to accurately reflects the trends of fission cross sections when sufficient training data are provided.
Zhi-Qiang Chen, Pei-Yan Zhang, Rui Han, Roy Wada, Guo-Yu Tian, Bing-Yan Liu, Hui Sun, Xin Zhang, Rui Guo, Ze-Kun Zhang, Qin Li, Fu-Dong Shi
CURRENT ISSUE
Nuclear Science and TechniquesVol.37, No.7
Cover Gallery
International Workshop on Nuclear Dynamics
Nuclear Physicist
Invited Review Articles
Table of Content
Research Story
Unraveling Asymmetric Collisions with Tsallis Thermodynamics
Speedy Nuclear Measurements for Cleaner Energy
Bromine's Neutron Capture and the s-Process
Nondestructive Neutron Techniques boosts Material Research and Preservation
Speedy Nuclear Measurements for Cleaner Energy
Advancing Particle Analysis with Intelligent Algorithms
Exploring Nuclear Energy with Lithium Reactions
Innovative Neutronics Modeling to Maximize Plutonium-238 Output
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Open Seminar in Frontier of Nuclear Physics
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