NSR Query Results
Output year order : Descending NSR database version of April 27, 2024. Search: Author = D.Peng Found 6 matches. 2023MA48 Phys.Rev. C 108, 044606 (2023) C.-W.Ma, X.-X.Chen, X.-B.Wei, D.Peng, H.-L.Wei, Y.-T.Wang, J.Pu, K.-X.Cheng, Y.-F.Guo, C.-Y.Qiao Systematic behavior of fragments in Bayesian neural network models for projectile fragmentation reactions
doi: 10.1103/PhysRevC.108.044606
2022MA39 Chin.Phys.C 46, 074104 (2022) C.-W.Ma, X.-B.Wei, X.-X.Chen, D.Peng, Y.-T.Wang, J.Pu, K.-X.Cheng, Y.-F.Guo, H.-L.Wei Precise machine learning models for fragment production in projectile fragmentation reactions using Bayesian neural networks
doi: 10.1088/1674-1137/ac5efb
2022PE09 J.Phys.(London) G49, 085102 (2022) D.Peng, H.-L.Wei, X.-X.Chen, X.-B.Wei, Y.-T.Wang, J.Pu, K.-X.Cheng, C.-W.Ma Bayesian evaluation of residual production cross sections in proton-induced nuclear spallation reactions NUCLEAR REACTIONS 1H(36Ar, X), (40Ar, X), (40Ca, X), (56Fe, X), (93Nb, X), (93Zr, X), (107Pd, X), (90Sr, X), (136Xe, X), (137Cs, X), (138Ba, X), (197Au, X), E<2.6 GeV/nucleon; analyzed available data; deduced accurate and complete energy-dependent residual σ using a simplified EPAX formula (sEPAX), the Bayesian neural network (BNN) technique.
doi: 10.1088/1361-6471/ac7069
2021QU02 Phys.Rev. C 103, 044607 (2021) G.Qu, Y.Huang, D.Peng, Z.Xu, W.Lin, H.Zheng, G.Tian, R.Han, C.Ma, M.Huang, P.Ren, J.Han, Z.Yang, X.Liu, R.Wada Abnormal flow of α-particles in heavy-ion collisions at intermediate energies NUCLEAR REACTIONS 12C(12C, X), E=50 MeV/nucleon; calculated differential σ(θ, E(α)), average in-plane momentum per nucleon as a function of the scaled rapidity, flow as a function of atomic number (Z=1-6), time evolution of flow for Z=1-6 fragments. 40Ca(40Ca, X), E=35 MeV/nucleon; Ni(Ar, X), (Ni, X), E=32-95 MeV/nucleon; calculated flow for proton, α, Z=3-5 and Z≥6 fragments as function of incident energy. Improved antisymmetrized molecular dynamics model with Fermi motion (AMD-FM) and the statistical decay code Gemini in the nucleon-nucleon collision process. Comparison with experimental data from GANIL and Texas A and M facilities. Investigated experimentally observed abnormal α transverse flow behavior in heavy-ion collisions at intermediate energies.
doi: 10.1103/PhysRevC.103.044607
2020MA01 Chin.Phys.C 44, 014104 (2020) C.-W.Ma, D.Peng, H.-L.Wei, Z.-M.Niu, Y.-T.Wang, R.Wada Isotopic cross-sections in proton induced spallation reactions based on the Bayesian neural network method NUCLEAR REACTIONS 36,40Ar, 40Ca, 56Fe, 136Xe, 197Au, 208Pb, 238U(p, X), E=200-1500 MeV/nucleon; analyzed available data; deduced σ using the Bayesian neural network (BNN) method.
doi: 10.1088/1674-1137/44/1/014104
2020MA61 Chin.Phys.C 44, 124107 (2020) C.-W.Ma, D.Peng, H.-L.Wei, Y.-T.Wang, J.Pu A Bayesian-neural-network prediction for fragment production in proton induced spallation reaction NUCLEAR REACTIONS 36,40Ar, 40Ca, 56Fe, 136Xe, 197Au, 208Pb, 238U(p, X), E<1000 MeV/nucleon; analyzed available data; deduced fragment production in spallation reactions yields key infrastructure data, σ, parameters using the empirical SPACS parameterizations, a Bayesian-neural-network (BNN) approach.
doi: 10.1088/1674-1137/abb657
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