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Deep Learning Exotic Hadrons

High Energy Physics - Phenomenology 2022-05-18 v2 High Energy Physics - Experiment Nuclear Theory

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 Pc(4312)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.

Keywords

Cite

@article{arxiv.2110.13742,
  title  = {Deep Learning Exotic Hadrons},
  author = {JPAC Collaboration and L. Ng and L. Bibrzycki and J. Nys and C. Fernandez-Ramirez and A. Pilloni and V. Mathieu and A. J. Rasmusson and A. P. Szczepaniak},
  journal= {arXiv preprint arXiv:2110.13742},
  year   = {2022}
}

Comments

Manuscript: 5 pages, 5 figures, 1 table; Supplemental material: 7 pages, 7 figures, 1 table

R2 v1 2026-06-24T07:12:09.957Z