Title: Machine Learning the Ground State Masses of Atomic Nuclei

Abstract: Machine Learning and Artificial Intelligence methods offer wide applicability to a class of problems known collectively as “inverse problems.” The solution to such problems involves the calculation of the causal factors that are responsible for a set of observations. Inverse problems are frequently encountered in nuclear physics, where measurements exist and these observations must be reconciled with theoretical models and interpretation. I will discuss a probabilistic machine learning technique applied to the binding energy of atomic nuclei. The set of observations comes from the Atomic Mass Evaluation (AME), which totals over 2000 data points. I show that inclusion of physics-based inputs as well as physics-informed training yields neural networks that are capable of describing these observations with a high degree of precision. Because the method is stochastic, it also provides an estimate of uncertainty for each prediction. I discuss the capacity of such modeling to extrapolate into regions of unmeasured nuclei that is needed for applications such as astrophysics.

Host: Gail McLaughlin