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<p>The fragment yields in photon-induced fission reactions of thorium (Th) isotopes are important for modern nuclear energy applications and for understanding the evolution of the nuclear structures of their isotopic chains. Bayesian neural network (BNN) models were constructed to describe the fragment yields in photonuclear fission reactions of thorium isotopes, ranging from <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(^{216}\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>216</mn> </mmultiscripts> </math></EquationSource> </InlineEquation>Th to <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(^{232}\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>232</mn> </mmultiscripts> </math></EquationSource> </InlineEquation>Th, especially those of <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(^{232}\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>232</mn> </mmultiscripts> </math></EquationSource> </InlineEquation>Th, at various incident photon energies. The predicted results of the optimized BNN models were in good agreement with the measured data for these reactions. The double-layer BNN models successfully illustrated the systematic transition from asymmetric to symmetric fission in thorium isotopes, including the associated odd-even effects, energy dependence, and leftward shift in mass yield distributions. The developed BNN models provide a new tool for predicting the fragment yields in thorium photonuclear fission reactions.</p>

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Bayesian evaluation of photofission product yields of Th isotopic chains

  • 期刊论文

  • 发表日期：

    2026年4月22日

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  • Meng-Die Zhou,
  • Chun-Yuan Qiao,
  • Pu Jiao,
  • Hong-Wei Wang,
  • Hui-Ling Wei,
  • Gong-Tao Fan,
  • Jin-Gen Chen,
  • Chun-Wang Ma

摘要

The fragment yields in photon-induced fission reactions of thorium (Th) isotopes are important for modern nuclear energy applications and for understanding the evolution of the nuclear structures of their isotopic chains. Bayesian neural network (BNN) models were constructed to describe the fragment yields in photonuclear fission reactions of thorium isotopes, ranging from \(^{216}\) 216 Th to \(^{232}\) 232 Th, especially those of \(^{232}\) 232 Th, at various incident photon energies. The predicted results of the optimized BNN models were in good agreement with the measured data for these reactions. The double-layer BNN models successfully illustrated the systematic transition from asymmetric to symmetric fission in thorium isotopes, including the associated odd-even effects, energy dependence, and leftward shift in mass yield distributions. The developed BNN models provide a new tool for predicting the fragment yields in thorium photonuclear fission reactions.