Abstract
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 distinguishing even the lowest channel threshold-enhancement of the nucleon-nucleon system using only the cross-sections. The difficulty lies in the proximity of either a bound or virtual state pole to the threshold which creates an almost identical structure in the scattering region. Identifying the nature of the pole causing the enhancement falls under the general classification problem and supervised machine learning using a feed-forward neural network is known to excel in this task. In this study, we discuss the basic idea behind deep neural network and how it can be used to identify the nature of the pole causing the enhancement. The applicability of the trained network can be explored by using an exact separable potential model to generate a validation dataset. We find that within some acceptable range of the cut-off parameter, the neural network gives high accuracy of inference. The result also reveals the important role played by the background singularities in the training dataset. Finally, we apply the method to nucleon-nucleon scattering data and show that the network was able to give the correct nature of pole, i.e. virtual pole for \({}^1S_0\) partial cross-section and bound state pole for \({}^3S_0\).
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Acknowledgements
This study is supported in part by JSPS KAKENHI Grant No. JP17K14287, and by MEXT as “Priority Issue on Post-K computer” (Elucidation of the Fundamental Laws and Evolution of the Universe) and SPIRE (Strategic Program for Innovative Research). DLBS is supported in part by the UP OVPAA FRASDP and DOST-PCIEERD postdoctoral research grant. YI is partly supported by JSPS KAKENHI Nos. JP17K14287 (B) and 21K03555 (C). AH is supported in part by JSPS KAKENHI No. JP17K05441 (C) and Grants-in-Aid for Scientific Research on Innovative Areas, No. 18H05407 and 19H05104.
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Sombillo, D.L.B., Ikeda, Y., Sato, T. et al. Classifying Near-Threshold Enhancement Using Deep Neural Network. Few-Body Syst 62, 52 (2021). https://doi.org/10.1007/s00601-021-01642-z
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DOI: https://doi.org/10.1007/s00601-021-01642-z