Classifying the pole of an amplitude using a deep neural network

Denny Lane B. Sombillo, Yoichi Ikeda, Toru Sato, Atsushi Hosaka

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Most of the exotic resonances observed in the past decade appear as a peak structure near some threshold. These near-threshold phenomena can be interpreted as genuine resonant states or enhanced threshold cusps. Apparently, there is no straightforward way of distinguishing the two structures. In this work, we employ the strength of deep feed-forward neural network in classifying objects with almost similar features. We construct a neural network model with scattering amplitude as input and the nature of a pole causing the enhancement as output. The training data is generated by an S-matrix satisfying the unitarity and analyticity requirements. Using the separable potential model, we generate a validation data set to measure the network's predictive power. We find that our trained neural network model gives high accuracy when the cutoff parameter of the validation data is within 400-800 MeV. As a final test, we use the Nijmegen partial wave and potential models for nucleon-nucleon scattering and show that the network gives the correct nature of the pole.

Original languageEnglish
Article number016024
JournalPhysical Review D
Volume102
Issue number1
DOIs
Publication statusPublished - Jul 1 2020

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy (miscellaneous)

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