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NSR database version of April 27, 2024.

Search: Author = Z.A.Wang

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2022WA26      Phys.Rev. C 106, L021304 (2022)

Z.A.Wang, J.C.Pei, Y.J.Chen, C.Y.Qiao, F.R.Xu, Z.G.Ge, N.C.Shu

Bayesian approach to heterogeneous data fusion of imperfect fission yields for augmented evaluations

NUCLEAR REACTIONS ²³⁸U(n, F), E<20 MeV; analyzed experimental data; calculated cumulative fission yields of ⁹⁹Mo, ¹³⁵Xe, ¹⁴⁰Ba, ¹⁴⁷Nd fragments, independent fission yields. Bayesian neural networks (BNNs) algorithm for machine learning.

doi: 10.1103/PhysRevC.106.L021304
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2021QI04      Phys.Rev. C 103, 034621 (2021)

C.Y.Qiao, J.C.Pei, Z.A.Wang, Y.Qiang, Y.J.Chen, N.C.Shu, Z.G.Ge

Bayesian evaluation of charge yields of fission fragments of ²³⁹U

NUCLEAR REACTIONS ^232,233Th, ²³⁹Pu(n, F), E=14 MeV; ²³⁹Pu, ²⁴⁴Cm(n, F), E=0.5 MeV; ²⁵⁵Fm(n, F), E=0.025 eV; analyzed one-layer and two-layer Bayesian neural network (BNN) learning results of charge yields taken from JENDL. ²³⁵U(n, F), E=0.025 eV and 0.5, 14 MeV; predicted BNN fission charge yields. ²³⁸U(n, F)²³⁹U^*, E=0.5 MeV; calculated and evaluated BNN fission charge yields. Double-layered Bayesian neural network (BNN) to learn and predict charge yields of fission fragments; deduced better performance of double-layer network better than that of the single-layer network with same number of neurons. Comparison with experimental data.

doi: 10.1103/PhysRevC.103.034621
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2021WA52      Phys.Rev. C 104, 064608 (2021)

Z.-A.Wang, J.Pei

Optimizing multilayer Bayesian neural networks for evaluation of fission yields

NUCLEAR REACTIONS ²³⁵U(n, F), E=0.5, 3.6, 4.49, 14 MeV; ²²⁹Th, ²⁴²Pu(n, F), E=0.01 MeV; ²³⁴U(n, F), E=14 MeV; ²³⁸U(n, F), E=0.5 MeV; analyzed and evaluated experimental data for induced fission yields using multilayer Bayesian neural networks (BNN) techniques with learning performances using different activation functions such as sine, tanh, sigmoid, and ReLU to improve their performance for evaluations of fission yields. ²³⁸U(n, F)²³⁹U^*; evaluated fission yields of compound nucleus with experimental data taken from GEF and ENDF libraries using double-layer BNN evaluations without and with penalty on negative values.

doi: 10.1103/PhysRevC.104.064608
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2019WA26      Phys.Rev.Lett. 123, 122501 (2019)

Z.-A.Wang, J.Pei, Y.Liu, Y.Qiang

Bayesian Evaluation of Incomplete Fission Yields

NUCLEAR REACTIONS ^227,229,232Th, ²³¹Pa, ^{232,233,234,235,236,237,238}U, ^237,238Np, ^{238,239,240,241,242}Pu, ^241,243Am, ^{242,243,244,245,246,248}Cm, ^249,251Cf, ²⁵⁴Es, ²⁵⁵Fm(n, F), E not given; analyzed available data using from JENDL library; deduced missing fission yield values using the Bayesian neural network (BNN) approach.

doi: 10.1103/PhysRevLett.123.122501
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