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Most of exotic resonances observed in the past decade appear as peakstructure near some threshold. These near-threshold phenomena can beinterpreted as genuine resonant states or enhanced threshold cusps. Apparently,there is no straightforward way of distinguishing the two structures. In thiswork, we employ the strength of deep feed-forward neural network in classifyingobjects with almost similar features. We construct a neural network model withscattering amplitude as input and nature of pole causing the enhancement asoutput. The training data is generated by an S-matrix satisfying the unitarityand analyticity requirements. Using the separable potential model, we generatea validation data set to measure the network's predictive power. We find thatour trained neural network model gives high accuracy when the cut-off parameterof the validation data is within $400$-$800\mbox{ MeV}$. As a final test, weuse the Nijmegen partial wave and potential models for nucleon-nucleonscattering and show that the network gives the correct nature of pole.

Classifying Pole of Amplitude Using Deep Neural Network