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NSR database version of May 20, 2024.

Search: Author = Y.A.Uncu

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2024OZ01      Appl.Radiat.Isot. 204, 111115 (2024)

H.Ozdogan, Y.A.Uncu, M.Sekerci, A.Kaplan

Neural network predictions of (α, n) reaction cross sections at 18.5±3 MeV using the Levenberg-Marquardt algorithm

NUCLEAR REACTIONS ⁴¹K, ⁵¹V, ⁵⁹Co, ⁶²Ni, ⁶³Cu, ^64,66,68Zn, ^69,71Ga, ^70,72Ge, ⁷⁶Se, ⁸¹Br, ⁸⁶Sr, ⁹³Nb, ^92,94Mo, ¹⁰⁰Ru, ¹⁰³Rh, ^107,109Ag, ¹¹⁴Cd, ¹¹³In, ^114,116,124Sn, ^121,123Sb, ¹³⁹La, ¹⁵⁰Nd, ¹⁵⁹Tb, ¹⁶⁵Ho, ¹⁶⁹Tm, ¹⁸¹Ta, ¹⁹¹Ir, ¹⁹⁷Au, ²⁰⁹Bi(α, n), E=18.5 MeV; analyzed available data; deduced σ.

doi: 10.1016/j.apradiso.2023.111115
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2023OZ01      Appl.Radiat.Isot. 192, 110609 (2023)

H.Ozdogan, Y.A.Uncu, M.Sekerci, A.Kaplan

Estimations for (n, α) reaction cross sections at around 14.5 MeV using Levenberg-Marquardt algorithm-based artificial neural network

NUCLEAR REACTIONS ⁴⁰Ar, ^39,41K, ⁴⁰Ca, ⁴⁴Ca, ⁴⁵Sc, ^48,50Ti, ⁵¹V, ^50,52,54Cr, ⁵⁵Mn, ⁵⁴Fe, ^56,57,58Fe, ^60,61,62Ni, ⁶⁴Ni, ^63,65Cu, ⁶⁴Zn, ^68,70Zn, ^69,71Ga, ^72,74Ge, ⁷⁵As, ^78,80Se, ^79,81Br, ⁸⁶Kr, ⁸⁵Rb, ⁸⁹Y, ^90,92,94,96Zr, ⁹³Nb, ^92,94,96Mo, ^97,98Mo, ¹⁰⁰Mo, ⁹⁹Tc, ^102,104Ru, ¹⁰³Rh, ^106,108Pd, ¹⁰⁶Cd, ^112,114,116Cd, ¹¹⁵In, ^{116,117,118,119,120}Sn, ^122,124Sn, ^126,128Te, ¹²⁷I, ¹³³Cs, ¹³⁸Ba, ¹³⁹La, ¹⁴²Ce, ¹⁴¹Pr, ^144,146,148Nd, ^150,152,154Sm, ^151,153Eu, ^156,158,160Gd, ¹⁵⁹Tb, ^162,164Dy, ^168,170Er, ¹⁶⁹Tm, ^172,174,176Yb, ^175,176Lu, ^178,180Hf, ¹⁸¹Ta, ^184,186W, ¹⁸⁷Re, ^190,192Os, ¹⁹¹Ir, ^194,196Pt, ¹⁹⁷Au, ²⁰⁰Hg, ^203,205Tl, ²⁰⁴Pb, ^206,207,208Pb, ²⁰⁹Bi(n, α), E ∼ 14.5 MeV; analyzed available data; deduced σ using Accurate artificial neural network (ANN) algorithms. Comparison with TALYS 1.95 calculations.

doi: 10.1016/j.apradiso.2022.110609
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2023OZ02      Appl.Radiat.Isot. 199, 110922 (2023)

H.Ozdogan, Y.A.Uncu, M.Sekerci, A.Kaplan

Calculation of double differential neutron cross-sections of ⁵⁶Fe and ⁹⁰Zr isotopes

NUCLEAR REACTIONS ⁵⁶Fe, ⁹⁰Zr(p, Xn), E=22.2 MeV; calculated σ(θ, E) using the PHITS 3.22 Monte Carlo and the TALYS 1.95 codes. Comparison with available data.

doi: 10.1016/j.apradiso.2023.110922
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2022OZ02      Appl.Radiat.Isot. 184, 110162 (2022)

H.Ozdogan, Y.A.Uncu, M.Sekerci, A.Kaplan

Mass excess estimations using artificial neural networks

NUCLEAR REACTIONS ⁵⁶Fe(p, X), E<65 MeV; calculated σ(E) using RIPL3 data.

ATOMIC MASSES ^{45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72}Fe, ^{47,48,49,50,51,52,53,54,55,56,57,58}Co, ^{184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218}Bi, ^{188,189,190,191,192}Po; calculated mass excess. Comparison with available data.

doi: 10.1016/j.apradiso.2022.110162
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2021OZ01      Appl.Radiat.Isot. 169, 109581 (2021)

H.Ozdogan, Y.A.Uncu, O.Karaman, M.Sekerci, A.Kaplan

Estimations of giant dipole resonance parameters using artificial neural network

NUCLEAR REACTIONS ^{142,143,144,145,146}Nd(γ, n), E<22 MeV; calculated σ. ^144,148Sm, ¹⁹³Ir, ¹⁹⁵Pt, ¹⁹⁷Au, ²⁰²Hg, ^206,207,208Pb, ²⁰⁹Bi, ¹⁶O, ⁶⁵Cu, ⁶⁹Ga, ^70,72Ge, ^80,82Se, ⁸⁵Rb, ⁸⁸Sr, ⁸⁹Y, ^90,91,92,94Zr, ⁹³Nb, ^{92,94,96,98,100}Mo, ¹⁰³Rh, ¹⁰⁸Pd, ¹⁰⁷Ag, ¹¹⁴Cd, ¹¹⁵In, ^{116,117,118,119,120,124}Sn, ¹²¹Sb, ^{124,126,128,130}Te, ¹³³Cs, ¹³⁸Ba, ¹³⁹La, ^140,142Ce, ¹⁴¹Pr, ^{142,143,144,145,146}Nd; deduced giant dipole resonance parameters, resonance σ using accurate artificial neural network (ANN) algorithms.

doi: 10.1016/j.apradiso.2020.109581
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