分类： 物理学 >> 核物理学 提交时间： 2023-06-07
Zhang, Lianxin
Chen, Size
Zhang, Zaodi
Li, Taosheng
Peng, Chuan
Ren, Lei
Zhang, Rui
Xiao. Dan
Zhang. Yong
摘要： Owing to the immobility of traditional reactors and spallation neutron sources, the demand for compact thermal neutron radiography (CTNR) based on accelerator neutron sources has rapidly increased in industrial applications. Recently, thermal neutron radiography experiments based on a D-T neutron generator performed by Hefei Institutes of Physical Science indicated a significant resolution deviation between the experimental results and the values calculated using the traditional resolution model. The experimental result was up to 23% lower than the calculated result, which hinders the achievement of the design goal of a compact neutron radiography system. A GEANT4 Monte Carlo code was developed to simulate the CTNR process, aiming to identify the key factors leading to resolution deviation. The effects of a low collimation ratio and high-energy neutrons were analyzed based on the neutron beam environment of the CTNR system. The results showed that the deviation was primarily caused by geometric distortion at low collimation ratios and radiation noise induced by high 1 energy neutrons. Additionally, the theoretical model was modified by considering the imaging position and radiation noise factors. The modified theoretical model was in good agreement with the experimental results, and the maximum deviation was reduced to 4.22%. This can be useful for the high-precision design of CTNR systems.
分类： 天文学 >> 天文学 提交时间： 2024-03-29 合作期刊: 《Research in Astronomy and Astrophysics》
摘要： Pulsar search is always the basis of pulsar navigation, gravitational wave detection and other research topics. Currently, the volume of pulsar candidates collected by the Five-hundred-meter Aperture Spherical radio Telescope (FAST) shows an explosive growth rate that has brought challenges for its pulsar candidate filtering system. Particularly, the multi-view heterogeneous data and class imbalance between true pulsars and non-pulsar candidates have negative effects on traditional single-modal supervised classification methods. In this study, a multi-modal and semi-supervised learning based on a pulsar candidate sifting algorithm is presented, which adopts a hybrid ensemble clustering scheme of density-based and partition-based methods combined with a feature-level fusion strategy for input data and a data partition strategy for parallelization. Experiments on both High Time Resolution Universe Survey II (HTRU2) and actual FAST observation data demonstrate that the proposed algorithm could excellently identify pulsars: On HTRU2, the precision and recall rates of its parallel mode reach 0.981 and 0.988 respectively. On FAST data, those of its parallel mode reach 0.891 and 0.961, meanwhile, the running time also significantly decreases with the increment of parallel nodes within limits. Thus, we can conclude that our algorithm could be a feasible idea for large scale pulsar candidate sifting for FAST drift scan observation.
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