3 papers
nucl-th2025
Reducing parametric uncertainties through information geometry methods
M. Imbrišak, A. E. Lovell, M. R. Mumpower
Information geometry is a study of applying differential geometry methods to challenging statistical problems, such as uncertainty quantification. In this work, we use information…
nucl-th2025
An optical-lensing inspired data thinning method for nuclear cross section data
M. Imbrišak, A. E. Lovell, M. R. Mumpower
In the study of nuclear cross sections, the computational demands of data assimilation methods can become prohibitive when dealing with large data sets. We have developed a novel v…
nucl-th2023
Bayesian averaging for ground state masses of atomic nuclei in a Machine Learning approach
M. R. Mumpower, M. Li, T. M. Sprouse +3
We present global predictions of the ground state mass of atomic nuclei based on a novel Machine Learning (ML) algorithm. We combine precision nuclear experimental measurements tog…