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A Deep Learning Framework for Disentangling Triangle Singularity and Pole-Based Enhancements
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abstract
Enhancements in the invariant mass distribution or scattering cross-section are usually associated with resonances. However, the nature of exotic signals found near hadron-hadron thresholds remain a puzzle today due to the presence of experimental uncertainties. In fact, a purely kinematical triangle diagram is also capable of producing similar structures, but do not correspond to any unstable quantum state. In this paper, we report for the first time, that a deep neural network can be trained to distinguish triangle singularity from pole-based enhancements with a reasonably high accuracy of discrimination between the two seemingly identical line shapes. We also identify the type of triangle enhancement that can be misidentified as a dynamic pole structure. We apply our method to confirm that the $P_\psi^N(4312)^+$ state is not due to a triangle singularity, but is more consistent with a pole-based interpretation, as determined solely through pure line-shape analysis. Lastly, we explain how our method can be used as a model-selection framework useful in studying other exotic hadron candidates.
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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Deep Neural Network Driven Simulation Based Inference Method for Pole Position Estimation under Model Misspecification
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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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.
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Analysis of hidden-charm pentaquarks as triangle singularities via deep learning
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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