22 citations · 37 across the 3 of their papers we have counts for
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nucl-th2023
Probabilistic neural networks for improved analyses with phenomenological models
C. H. Kim, K. Y. Chae, M. S. Smith +5
Physics models typically contain adjustable parameters to reproduce measured data. While some parameters correspond directly to measured features in the data, others are unobservab…
nucl-th2017★ 22 cited
Microscopic core-quasiparticle coupling model for spectroscopy of odd-mass nuclei
S. Quan, W. P. Liu, Z. P. Li +1
Predictions of the spectroscopic properties of low-lying states are critical for nuclear structure studies, but are problematic for nuclei with an odd nucleon due to the interplay…