https://doi.org/10.1140/epja/s10050-025-01636-z
Regular Article - Experimental Physics
A method for determining the charge yield distribution of fission fragments based on K-means clustering algorithm
1
School of Nuclear Science and Technology, Lanzhou University, 730000, Lanzhou, China
2
Engineering Research Center for Neutron Application Technology, Ministry of Education, Lanzhou University, 730000, Lanzhou, China
3
MOE Frontiers Science Center for Rare Isotopes, Lanzhou University, 730000, Lanzhou, China
4
State Key Laboratory of Chemistry for NBC Hazards Protection, Frontiers Science Center for Rare Isotopes, School of Nuclear Science and Technology, Lanzhou University, 730000, Lanzhou, China
a
weizheng@lzu.edu.cn
b
zhyu@lzu.edu.cn
Received:
8
April
2025
Accepted:
3
July
2025
Published online:
14
July
2025
Measurement of the fission fragments charge yield distribution has been a longstanding challenge in independent yield distribution research, as traditional particle charge identification methods prove inadequate for this application. In this work, we propose a novel method for determining charge yield distribution using the K-means clustering algorithm, which relies on the simulation data of the multi-software coupled simulation of the velocity-kinetic energy (v–E) method fission spectrometer. Simulations of the fission reaction of 238U induced by 14 MeV neutron were conducted. By processing the pulse waveforms of individual mass chains with K-means clustering algorithm, the charge yield distribution is obtained. The results indicate that variations in the pulse signals of fission fragments within the same mass chain, following timing shift correction, primarily originate from charge differences. The influence of the number of clusters on clustering performance was analyzed, demonstrating that optimal results were achieved at k = 4, with a Root Mean Square Error (RMSE) of 6.95 × 10–3 and an Error Ratio (ER) of 29.26%. Reduced sampling rates resulted in progressively degraded clustering performance. The method based on the K-means clustering algorithm developed in this work demonstrates high accuracy in charge yield distribution, thereby establishing a foundation for future high-quality independent yield distribution measurements.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
