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NSR database version of May 1, 2024.

Search: Author = R.Utama

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2018UT01      Phys.Rev. C 97, 014306 (2018)

R.Utama, J.Piekarewicz

Validating neural-network refinements of nuclear mass models

ATOMIC MASSES ^53,54Ca, ^56,57Sc, ⁶⁴Cr, ⁶²Mn, ⁵²Co, ⁵⁶Cu, ⁸²Zn, ⁸⁶Ge, ⁹¹Se, ⁸²Zn, ¹⁰⁰Rb, ¹⁰⁵Y, ^82,106,107Zr, ^84,110Nb, ^114,115Tc, ¹²¹Rh, ¹²³Pd, ^129,131Cd, ¹³⁸Sb, ¹⁴¹I, ¹⁴⁹Ba, ^150,151La, ¹³⁷Eu, ¹⁹⁰Tl, ²¹⁵Pb, ¹⁹⁴Bi, ¹⁹⁸At, ^{197,198,202,232,233}Fr, ²⁰¹Ra, ^205,206Ac, ^{215,216,221,222}U; ^132,133,134Cd, ^{133,134,135,136,137}In, ^136,138Sn; calculated total binding energies using the microscopic HFB-19-Bayesian neural network (BNN), and mic-mac model of Duflo and Zuker (DZ) with Bayesian neural network (BNN), and compared with various theoretical mass formulas (HFB-19, DZ, FRDM-2012, HFB-27 and WS3), and with experimental values in AME-2016; deduced root-mean-square deviations, refinements in Bayesian neural network (BNN) analysis of mass models.

doi: 10.1103/PhysRevC.97.014306
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2017UT01      Phys.Rev. C 96, 044308 (2017)

R.Utama, J.Piekarewicz

Refining mass formulas for astrophysical applications: A Bayesian neural network approach

ATOMIC MASSES Z=20-90, N=20-220; ^130,131,132Pd, ^{132,133,134,135,136,137,138}Cd, ^{133,134,135,136,137,138}In, ^136,138Sn; analyzed mass formulas for proton and neutron drip lines; deduced refined mass tables for two existing mass models, microscopic and the mic-mac type, using HFB19 for former and 28-parameter Duflo-Zuker model for the latter with the Bayesian neural network (BNN) approach. Comparison with mass models and with AME-2012 evaluation.

doi: 10.1103/PhysRevC.96.044308
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2016PI02      Acta Phys.Pol. B47, 659 (2016)

J.Piekarewicz, R.Utama

The Nuclear Physics of Neutron Stars

doi: 10.5506/APhysPolB.47.659
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2016UT01      Phys.Rev. C 93, 014311 (2016)

R.Utama, J.Piekarewicz, H.B.Prosper

Nuclear mass predictions for the crustal composition of neutron stars: A Bayesian neural network approach

ATOMIC MASSES A=40-240, Z=20-92; analyzed experimental masses from AME-2012 to deduce liquid-drop-model parameters and uncertainties, analyzed theoretical predictions of masses from different models such as Duflo and Zuker (DZ), Moller and Nix (MN), finite range droplet model (FRDM), HFB19 and HFB21 microscopic models using a novel Bayesian neural network (BNN) formalism; deduced a mass model that is used to predict the composition of the outer crust of a neutron star. ^{96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112}Kr; analyzed mass predictions from five mass models, and from the BNN-improved formalism with comparison to AME-2012 evaluation. Relevance to prediction of composition of outer crust of a neutron star.

doi: 10.1103/PhysRevC.93.014311
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2016UT02      J.Phys.(London) G43, 114002 (2016)

R.Utama, W.-C.Chen, J.Piekarewicz

Nuclear charge radii: density functional theory meets Bayesian neural networks

NUCLEAR STRUCTURE ^87,88,90Y, ^189,195,208Pb, ^207,208Bi, ^{124,128,132,136}Sn; calculated nuclear charge radii. Comparison with available data.

doi: 10.1088/0954-3899/43/11/114002
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