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Deep Learning Exotic Hadrons

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arxiv 2110.13742 v2 pith:SQD4ERIX submitted 2021-10-26 hep-ph hep-exnucl-th

JPAC Collaboration: L. Ng , L. Bibrzycki , J. Nys , C. Fernandez-Ramirez , A. Pilloni , V. Mathieu , A.J. Rasmusson , A.P. Szczepaniak This is my paper · ORCID
classification hep-phhep-exnucl-th
keywords deepexoticanalysisappliedcandidatescollaborationdatadetermine
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abstract

We perform the first model independent analysis of experimental data using Deep Neural Networks to determine the nature of an exotic hadron. Specifically, we study the line shape of the $P_c(4312)$ signal reported by the LHCb collaboration and we find that its most likely interpretation is that of a virtual state. This method can be applied to other near-threshold resonance candidates.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Deep Neural Network Driven Simulation Based Inference Method for Pole Position Estimation under Model Misspecification

    hep-ph 2025-07 conditional novelty 5.0 of 10

    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.

  2. Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics

    hep-lat 2025-01 unverdicted novelty 1.0 of 10

    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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