25 citations · 75 across the 15 of their papers we have counts for
3 papers · 1 filter
Classifying near-threshold enhancement using deep neural network
Denny Lane B. Sombillo, Yoichi Ikeda, Toru Sato +1
One of the main issues in hadron spectroscopy is to identify the origin of threshold or near-threshold enhancement. Prior to our study, there is no straightforward way of distingui…
Unveiling the pole structure of S-matrix using deep learning
Denny Lane B. Sombillo, Yoichi Ikeda, Toru Sato +1
Particle scattering is a powerful tool to unveil the nature of various subatomic phenomena. The key quantity is the scattering amplitude whose analytic structure carries the inform…
Model independent analysis of coupled-channel scattering: a deep learning approach
Denny Lane B. Sombillo, Yoichi Ikeda, Toru Sato +1
We develop a robust method to extract the pole configuration of a given partial-wave amplitude. In our approach, a deep neural network is constructed where the statistical errors o…