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.
Highlights
Renewed interest in hadron spectroscopy started after the discovery of Xð3872Þ in 2003 [1]
We find that our trained neural network model gives high accuracy when the cutoff parameter of the validation data is within 400–800 MeV
It has been shown in [6,7,8,9] that threshold cusp can only produce a significant enhancement provided that there is some nearthreshold pole even if it is not located in the relevant region of unphysical sheet
Summary
Renewed interest in hadron spectroscopy started after the discovery of Xð3872Þ in 2003 [1]. The purpose of this paper is to address the origin of the sharp peak observed around the threshold of two-body hadron scattering problems. There has not been a method to distinguish the pole origin of peak structure around the threshold This is a difficult program because of the limited resolution of experimental data. In this work we demonstrate how a deep neural network can be applied to identify the pole origin of cross section enhancement. This includes defining the appropriate input-output data, setting up the network architecture, and generating the training dataset. As a first effort to apply deep learning in the classification of pole causing a cross section enhancement, we only consider here the single-channel scattering.
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