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NSR database version of May 21, 2024.

Search: Author = S.Akkoyun

Found 11 matches.

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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
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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
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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
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2023YE05      J.Phys.(London) G50, 055101 (2023)

C.M.Yesilkanat, S.Akkoyun

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
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2022AK04      Phys.Rev. C 105, 044309 (2022)

S.Akkoyun, A.Yakhelef

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
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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
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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
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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
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Data from this article have been entered in the XUNDL database. For more information, click here.


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
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Data from this article have been entered in the XUNDL database. For more information, click here.


2013BA30      Phys.Scr. 87, 065201 (2013)

T.Bayram, S.Akkoyun

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
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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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