26-31 July 2021
Mexico City
Mexico/General timezone
HADRON 2021 is over. Thanks for making it a success!
- hadron2021@nucleares.unam.mx
Contact information
Contribution Leading parallel
Mexico City
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Analysis Tools
Probing the pole configuration of scattering amplitude using deep learning
Speakers
- Dr. Denny Lane SOMBILLO
Primary authors
- Dr. Denny Lane SOMBILLO (Research Center for Nuclear Physics, Osaka University/ National Institute of Physics, University of the Philippines Diliman)
Co-authors
- Dr. Yoichi IKEDA (Department of Physics, Kyushu University)
- Dr. Toru SATO (Research Center for Nuclear Physics, Osaka University)
- Prof. Atsushi HOSAKA (Research Center for Nuclear Physics, Osaka University)
Abstract
We propose a deep learning method that identifies the pole configuration of a coupled-channel scattering, i.e., the number of nearby poles in each Riemann sheet associated with the structures in the amplitude. A generic parametrized S-matrix is used to generate the dataset and curriculum learning is used for the training. We apply our method to the elastic $\pi N$ scattering and found that the observed structures are caused by one pole in each nearby sheet and two poles in the distance sheet.