Modeling masses with an artificial neural network

Fall DNP

Contributed presentation on 10/2022

We present a new model of masses based on an artificial neural network. Using a randomized fraction (~20%) of the Atomic Mass Evaluation we predict ~80% of measured masses to with an accuracy of approximately 300 keV. We employ a Mixture Density Network to produce probabilistic output. Thus our methodology also provides confidence intervals for each prediction. Addition of a physical constraint, here the Garvey-Kelson relations, greatly improves the predictive capabilities of this modeling.


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Matthew Mumpower
Los Alamos National Lab
MS B283
TA-3 Bldg 123
Los Alamos, NM 87545

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