<ul data-eligibleForWebStory="false"><li>Identifying new hadronic states is challenging due to exotic signals near threshold arising from various physical mechanisms.</li><li>A machine learning approach has been introduced for classifying pole structures in S-matrix elements with uncertainty estimates.</li><li>The approach achieved a validation accuracy of nearly 95% by applying a rejection criterion based on predictive uncertainty.</li><li>The model generalizes to unseen experimental data, including identifying a genuine compact pentaquark in the presence of higher channel virtual state pole.</li></ul>

Learning Pole Structures of Hadronic States using Predictive Uncertainty Estimation

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