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NSR database version of May 8, 2024.

Search: Author = A.T.Mohan

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2022LO09      Phys.Rev. C 106, 014305 (2022)

A.E.Lovell, A.T.Mohan, T.M.Sprouse, M.R.Mumpower

Nuclear masses learned from a probabilistic neural network

ATOMIC MASSES Z=20-110, N=16-160; calculated atomic masses and S(n) using the probabilistic Mixture Density Network (MDN) for six models: M2, MS2, MS6, MS8, MS10, and MS12, and compared with evaluated atomic masses in AME2016 and theoretical masses in Moller's FRDM2012. Relevance to accuracy of the match to the training data, and providing physically meaningful extrapolations beyond the limits of experimental data.

doi: 10.1103/PhysRevC.106.014305
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2022MU14      Phys.Rev. C 106, L021301 (2022)

M.R.Mumpower, T.M.Sprouse, A.E.Lovell, A.T.Mohan

Physically interpretable machine learning for nuclear masses

ATOMIC MASSES ^{137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162}Nd; calculated masses. Results obtained with probabilistic machine learning algorithm. Comparison to AME2016.

doi: 10.1103/PhysRevC.106.L021301
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2020LO14      J.Phys.(London) G47, 114001 (2020)

A.E.Lovell, A.T.Mohan, P.Talou

Quantifying uncertainties on fission fragment mass yields with mixture density networks

RADIOACTIVITY ²⁵²Cf(SF); calculated fission yields distributions and uncertainties. The mixture density network (MDN).

doi: 10.1088/1361-6471/ab9f58
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