A CNN trained on synthetic line shapes from a uniformized S-matrix classifies the CLAS Sigma-pi spectrum as the two-pole Lambda(1405) structure on the second Riemann sheet.
Pole structure of $P_\psi^N(4312)^+$ via machine learning and uniformized S-matrix
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abstract
We probed the pole structure of the $P_\psi^{N}(4312)^{+}$ using a trained deep neural network. The training dataset was generated using uniformized independent S-matrix poles to ensure that the obtained interpretation is as model-independent as possible. To prevent possible ambiguity in the interpretation of the pole structure, we included the contribution from the off-diagonal element of the S-matrix. Five out of the six neural networks we trained favor $P_\psi^{N}(4312)^{+}$ as possibly having a three-pole structure, with one pole on each of the unphysical sheets - a first in its report. The two poles can be associated to a pole-shadow pair which is a characteristic of a true resonance. On the other hand, the last pole is most likely associated with the coupled-channel effect. The combined effect of these poles produced a peak below the $\Sigma^{+}_C\bar{D}^0$ which mimic the line shape of a hadronic molecule.
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Line shape analysis of $\Lambda(1405)$ in $\gamma p \rightarrow K^+\Sigma^-\pi^+$ reaction using convolutional neural network
A CNN trained on synthetic line shapes from a uniformized S-matrix classifies the CLAS Sigma-pi spectrum as the two-pole Lambda(1405) structure on the second Riemann sheet.