NSR Query Results
Output year order : Descending NSR database version of April 11, 2024. Search: Author = S.Akkoyun Found 11 matches. 2024AK02 Nucl.Instrum.Methods Phys.Res. B549, 165293 (2024) S.Akkoyun, C.M.Yesilkanat, T.Bayram Machine learning predictions for cross-sections of 43, 44Sc radioisotope production by alpha-induced reactions on Ca target NUCLEAR REACTIONS Ca(α, X)43Sc/44Sc, 40Ca(α, p), E<50 MeV; analyzed available data; deduced production σ with Bayesian Regularized Neural Network, Support Vector Regression and Stacked Ensemble Learning methods.
doi: 10.1016/j.nimb.2024.165293
2023AK01 Appl.Radiat.Isot. 191, 110554 (2023) Ser.Akkoyun, N.Amrani, T.Bayram Neural network predictions of (n, 2n) reaction cross-sections at 14.6 MeV incident neutron energy NUCLEAR REACTIONS 43Ca, 49Ti, 50V, 53Cr, 57Fe, 61Ni, 67Zn, 73Ge, 77Se, 83Kr, 87Sr, 91Zr, 95,97Mo, 97,99Ru, 105Pd, 111,113Cd, 115,117,119Sn, 123,125Te, 129,131Xe, 135,137Ba, 138La, 143,145Nd, 147,149Sm, 155,157Gd, 161,163Dy, 167Er, 171,173Yb, 176Lu, 177,179Hf, 180Ta, 183W, 187,189Os, 195Pt, 199,201Hg, 207Pb(n, 2n), E=14.6 MeV; analyzed available data; deduced σ using neural network. Comparison with TALYS-1.95 and EMPIRE-3.2 calculations.
doi: 10.1016/j.apradiso.2022.110554
2023MU05 Phys.Rev. C 107, 034308 (2023) J.M.Munoz, S.Akkoyun, Z.P.Reyes, L.A.Pachon Predicting β-decay energy with machine learning RADIOACTIVITY Z=1-120(β-); calculated Q values. Machine learning algorithm utilizing data in magic numbers. AME2020 data was used to train the ML model and validate the results.
doi: 10.1103/PhysRevC.107.034308
2023YE05 J.Phys.(London) G50, 055101 (2023) Estimation of fission barrier heights for even-even superheavy nuclei using machine learning approaches NUCLEAR STRUCTURE Z=98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120; analyzed available data; deduced the fission barrier height information, the survival probabilities of super-heavy nuclei using five machine learning techniques, Cubist model, Random Forest, support vector regression, extreme gradient boosting and artificial neural network.
doi: 10.1088/1361-6471/acbaaf
2022AK04 Phys.Rev. C 105, 044309 (2022) Artificial-intelligence-supported shell-model calculations for light Sn isotopes NUCLEAR STRUCTURE 101Sn; calculated neutron single particle energies. 102,103,104,105,106,107,108Sn; calculated levels J, π. Artificial neural network method combined with shell-model calculations. Comparison to experimental data.
doi: 10.1103/PhysRevC.105.044309
2022KO11 Int.J.Mod.Phys. E31, 2250032 (2022) G.Kocak, S.Akkoyun, I.Boztosun, H.Dapo Energy transitions and half-life determinations of 47Sc isotopes from photonuclear reaction on Ti target RADIOACTIVITY 47Sc(β-) [from Ti(γ, X), E<18 MeV]; measured decay products, Eγ, Iγ; deduced γ-ray energies, levels, T1/2. Comparison with available data.
doi: 10.1142/S021830132250032X
2018BA28 Phys.Atomic Nuclei 81, 288 (2018) T.Bayram, S.Akkoyun, S.Senturk Adjustment of Non-linear Interaction Parameters for Relativistic Mean Field Approach by Using Artificial Neural Networks NUCLEAR STRUCTURE 40Ca, 88Sr, 132Sn, 208,214Pb; calculated binding energy, mass excess using using Relativistic Mean Field (RMF) model with Artificial Neural Network (ANN) method; deduced RMF model parameters; compared with published calculations and available data.
doi: 10.1134/S1063778818030043
2016AK04 Int.J.Mod.Phys. E25, 1650045 (2016) S.Akkoyun, T.Bayram, F.Dulger, H.Dapo, I.Boztosun Energy level and half-life determinations from photonuclear reaction on Ga target RADIOACTIVITY 68Ge(EC), 70,72Ge(β-) [from Ga(γ, X), E<18 MeV]; measured decay products, Eγ, Iγ; deduced T1/2. Comparison with available data.
doi: 10.1142/S0218301316500452
2016BA65 Int.J.Mod.Phys. E25, 1650107 (2016) T.Bayram, Se.Akkoyun, S.Uruk, H.Dapo, F.Dulger, I.Boztosun Transition energy and half-life determinations of photonuclear reaction products of erbium nuclei NUCLEAR REACTIONS Er(γ, X)161Ho, E<14 MeV; measured reaction products, Eγ, Iγ; deduced energy levels, J, π. Comparison with available data.
doi: 10.1142/S021830131650107X
2013BA30 Phys.Scr. 87, 065201 (2013) An analysis of E(5) shape phase transitions in Cr isotopes with covariant density functional theory NUCLEAR STRUCTURE 52,54,56,58,60,62,64,66Cr; calculated binding energies, quadrupole moments, potential energy surfaces, single-particle levels, ground state charge radii. Self-consistent RMF theory with effective interactions, comparison with available data.
doi: 10.1088/0031-8949/87/06/065201
2013VA14 Phys.Rev. C 88, 034312 (2013) V.Vandone, S.Leoni, G.Benzoni, N.Blasi, A.Bracco, S.Brambilla, C.Boiano, S.Bottoni, F.Camera, A.Corsi, F.C.L.Crespi, A.Giaz, B.Million, R.Nicolini, L.Pellegri, A.Pullia, O.Wieland, D.Bortolato, G.de Angelis, E.Calore, A.Gottardo, G.Maron, D.R.Napoli, D.Rosso, E.Sahin, J.J.Valiente-Dobon, D.Bazzacco, M.Bellato, E.Farnea, S.Lunardi, R.Menegazzo, D.Mengoni, P.Molini, C.Michelagnoli, D.Montanari, F.Recchia, C.A.Ur, A.Gadea, T.Huyuk, N.Cieplicka, A.Maj, M.Kmiecik, A.Atac, S.Akkoyun, A.Kaskas, P.-A.Soderstrom, B.Birkenbach, B.Cederwall, P.J.Coleman-Smith, D.M.Cullen, P.Desesquelles, J.Eberth, A.Gorgen, J.Grebosz, H.Hess, D.Judson, A.Jungclaus, N.Karkour, P.Nolan, A.Obertelli, P.Reiter, M.D.Salsac, O.Stezowski, Ch.Theisen, M.Matsuo, E.Vigezzi Global properties of K hindrance probed by the γ decay of the warm rotating 174W nucleus NUCLEAR REACTIONS 128Te(50Ti, 4n)174W, E=217 MeV; measured Eγ, Iγ, γγ-coin, prompt and delayed γ spectra using AGATA array at LNL-INFN facility; deduced high-K rotational bands, ridges in γγ-coin matrix, K-hindrance. Fluctuation analysis of low-K and high-K bands, moments of inertia. Monte Carlo simulation of γ-decay flow. Role of K-mixing on discrete bands due to temperature effects. Comparison with microscopic shell-model calculations with two-body residual interaction.
doi: 10.1103/PhysRevC.88.034312
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