Physics – Nuclear Physics – Nuclear Theory
Scientific paper
2006-03-12
Int.J.Mod.Phys.B20:5015-5029,2006
Physics
Nuclear Physics
Nuclear Theory
15 pages, 1 figure, 13th International Conference on Recent Progress in Many-Body Theories QMBT13
Scientific paper
10.1142/S0217979206036053
Advances in statistical learning theory present the opportunity to develop statistical models of quantum many-body systems exhibiting remarkable predictive power. The potential of such ``theory-thin'' approaches is illustrated with the application of Support Vector Machines (SVMs) to global prediction of nuclear properties as functions of proton and neutron numbers $Z$ and $N$ across the nuclidic chart. Based on the principle of structural-risk minimization, SVMs learn from examples in the existing database of a given property $Y$, automatically and optimally identify a set of ``support vectors'' corresponding to representative nuclei in the training set, and approximate the mapping $(Z,N) \to Y$ in terms of these nuclei. Results are reported for nuclear masses, beta-decay lifetimes, and spins/parities of nuclear ground states. These results indicate that SVM models can match or even surpass the predictive performance of the best conventional ``theory-thick'' global models based on nuclear phenomenology.
Clark John Willis
Li Haochen
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