2 papers
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…
physics.ins-det2023
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…