Experiments in machine learning of alpha-decay half-lives

Freitas, Paulo S. A.; Clark, John W.

http://hdl.handle.net/10400.13/2744

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Authors

Freitas, Paulo S. A.

Clark, John W.

Abstract(s)

Artificial neural networks are trained by a standard backpropagation learning algorithm with regularization to model and predict the systematics of alpha decay of heavy and superheavy nuclei. This approach to regression is implemented in two alternative modes: (i) construction of a statistical global model based solely on available experimental data for alpha-decay half-lives and Q-values, and (ii) modeling of the residuals between the predictions of state-of-the-art phenomenological model (specifically, the effective liquid-drop model (ELDM)) and experiment. Analysis of the results provides insights on the strengths and limitations of this application of machine learning (ML) to exploration of the nuclear landscape in regions beyond the valley of stability.