Commentary05 Feb 2024
Unbound 28O, the heaviest oxygen isotope observed: a cutting‑edge probe for testing nuclear models
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Research article07 May 2025
Deep learning-based compressed sampling reconstruction algorithm for digitizing intensive neutron ToF signals
Neutron time-of-flight (ToF) measurement is a highly accurate method for obtaining the kinetic energy of A neutron by measuring its velocity, but requires precise acquisition of the neutron signal arrival time. However, the high hardware costs and data burden associated with the acquisition of neutron ToF signals pose significant challenges. Higher sampling rates increase the data volume, data processing, and storage hardware costs. Compressed sampling can address these challenges, but it faces issues regarding optimal sampling efficiency and high-quality reconstructed signals. This paper proposes a revolutionary deep learning-based compressed sampling (DL-CS) algorithm for reconstructing neutron ToF signals that outperforms traditional compressed sampling methods. This approach comprises four modules: random projection, rising dimensions, initial reconstruction, and final reconstruction. Initially, the technique adaptively compresses neutron ToF signals sequentially using three convolutional layers, replacing random measurement matrices in traditional compressed sampling theory. Subsequently, the signals are reconstructed using a modified inception module, long short-term memory, and self-attention. The performance of this deep-compressed sampling method was quantified using the percentage root-mean-square difference, correlation coefficient, and reconstruction time. Experimental results showed that our proposed DL-CS approach can significantly enhance signal quality compared with other compressed sampling methods. This is evidenced by a percentage root-mean-square difference, correlation coefficient, and reconstruction time results of 5%, 0.9988, and 0.0108 s, respectively, obtained for sampling rates below 10% for the neutron ToF signal generated using an electron-beam-driven photoneutron source. The results showed that the proposed DL-CS approach significantly improves the signal quality compared with other compressed sampling methods, exhibiting excellent reconstruction accuracy and speed.
Xian-Guo Tuo, Qi-Biao Wang, Chao Deng, Shu-Jun Wang, Qin Hu, Ying-Hong Tang, Peng-Cheng Li, Bo Xie, Jian-Bo Yang
Research article07 May 2025
Cluster counting algorithm for the CEPC drift chamber using LSTM and DGCNN
The particle identification (PID) of hadrons plays a crucial role in particle physics experiments, especially in flavor physics and jet tagging. The cluster-counting method, which measures the number of primary ionizations in gaseous detectors, is a promising breakthrough in PID. However, developing an effective reconstruction algorithm for cluster counting remains challenging. To address this challenge, we propose a cluster-counting algorithm based on long short-term memory and dynamic graph convolutional neural networks for the CEPC drift chamber. Experiments on Monte Carlo simulated samples demonstrate that our machine-learning-based algorithm surpasses traditional methods. It improves the K/π separation of PID by 10%, meeting the PID requirements of CEPC.
Ming-Yi Dong, Zhe-Fei Tian, Guang Zhao, Ling-Hui Wu, Zhen-Yu Zhang, Xiang Zhou, Shui-Ting Xin, Shuai-Yi Liu, Gang Li, Sheng-Sen Sun
Research article07 May 2025
Single neutron super-resolution imaging based on neutron capture event detection and reconstruction
Neutron capture event imaging is a novel technique that has the potential to substantially enhance the resolution of existing imaging systems. This study provides a measurement method for neutron capture event distribution along with multiple reconstruction methods for super-resolution imaging. The proposed technology reduces the point-spread function of an imaging system through single-neutron detection and event reconstruction, thereby significantly improving imaging resolution. A single-neutron detection experiment was conducted using a highly practical and efficient 6LiF-ZnS scintillation screen of a cold neutron imaging device in the research reactor. In milliseconds of exposure time, a large number of weak light clusters and their distribution in the scintillation screen were recorded frame by frame, to complete single-neutron detection. Several reconstruction algorithms were proposed for the calculations. The location of neutron capture was calculated using several processing methods such as noise removal, filtering, spot segmentation, contour analysis, and local positioning. The proposed algorithm achieved a higher imaging resolution and faster reconstruction speed, and single neutron super-resolution imaging was realized by combining single neutron detection experiments and reconstruction calculations. The results show that the resolution of the 100 μm thick 6LiF-ZnS scintillation screen can be improved from 125 to 40 microns. This indicates that the proposed single-neutron detection and calculation method is effective and can significantly improve imaging resolution.
Hong-Li Chen, Hang Li, Yu-Hua Ma, Bin Tang, Wei Yin, Hong-Wen Huang, Xin Yang, He-Yong Huo, Yong Sun, Sheng Wang, Bin Liu, Run-Dong Li, Yang Wu
CURRENT ISSUE
Nuclear Science and TechniquesVol.36, No.7
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International Workshop on Nuclear Dynamics
Nuclear Physicist
Invited Review Articles
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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