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Classifying Pole of Amplitude Using Deep Neural Network
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abstract
Most of exotic resonances observed in the past decade appear as 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 nature of 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 cut-off parameter of the validation data is within $400$-$800\mbox{ MeV}$. 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 pole.
Forward citations
Cited by 4 Pith papers
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The $a_1(1420)$ in a Unitary Coupled-Channel Three-Body Approach
Unitary coupled-channel three-body model fitted to COMPASS data reproduces the a1(1420) enhancement via triangle singularity, indicating no genuine resonance pole is required.
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Simulation-based inference trained on synthetic scattering data yields rho(770) pole estimates closer to reference values than chi-squared minimization in the tested misspecification cases.
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A deep neural network trained on synthetic line shapes classifies the LHCb P_c(4457) signal as a resonance pole-shadow pair, ruling out the triangle singularity interpretation.
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Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
A perspective article reviewing physics-driven machine learning for inverse problems in QCD, without introducing new data, derivations, or quantitative results.
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