8 citations · 8 across the 6 of their papers we have counts for
6 papers
NuGNN: a Graph Neural Network for Nuclear Reaction Network Equations
C. H. Kim, K. Y. Chae, S. Ko +2
Nuclear reaction networks are a major computational bottleneck in astrophysical simulations when large isotope sets are required, because of the stiffness of the network equations…
Systematic Bayesian Evaluation of Resonance Parameters in 19Ne for the 15O(alpha,gamma)19Ne and 18F(p,alpha)15O Reactions
S. H. Kim, K. Y. Chae, C. H. Kim +2
We present a comprehensive evaluation of the nuclear structure properties of 19Ne using a novel and rigorous Bayesian statistical framework. Precise characterization of 19Ne resona…
Robust extrapolation using physics-related activation functions in neural networks for nuclear masses
C. H. Kim, K. Y. Chae, M. S. Smith
Given the importance of nuclear mass predictions, numerous models have been developed to extrapolate the measured data into unknown regions. While neural networks -- the core of mo…
First direct measurement constraining the Ar(,p)K reaction cross section for mixed hydrogen and helium burning in accreting neutron stars
J. Browne, K. A. Chipps, K. Schmidt +25
The rate of the final step in the astrophysical p-process, the Ar(,\textit{p})K reaction, suffers from large uncertainties due to lack of experimental data, des…
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…
Restoring Original Signal From Pile-up Signal using Deep Learning
C. H. Kim, S. Ahn, K. Y. Chae +2
Pile-up signals are frequently produced in experimental physics. They create inaccurate physics data with high uncertainty and cause various problems. Therefore, the correction to…