Analysis of hidden-charm pentaquarks as triangle singularities via deep learning
Darwin Alexander O. Co, Denny Lane B. Sombillo

TL;DR
This paper uses deep learning to analyze the nature of pentaquark signals, specifically distinguishing between triangle singularities and pole structures, and concludes that the observed data favor pole interpretations over triangle mechanisms.
Contribution
It introduces a deep neural network-based method for classifying line shapes in hadronic physics, providing an alternative to traditional fitting techniques for identifying the nature of near-threshold enhancements.
Findings
Triangle singularities are ruled out by experimental data.
Data favor pole-shadow pair interpretation for P_c cbar(4457)+.
Deep learning effectively distinguishes between different physical mechanisms.
Abstract
Identifying the nature of near-threshold enhancements is hindered by the limited resolution of experimental data leading to multiple conflicting interpretations. A prominent example of ambiguous line shape is the set of pentaquark signals observed by LHCb in 2019. Some of these signals can be interpreted as hadronic molecule, compact state, virtual state, or due to a kinematical triangle mechanism. In this work, we leverage the model-selection capability of deep neural networks to analyze and identify the nature of . We trained a set of deep neural networks using line shapes with enhancements produced by triangle singularities and those produced by nearby poles. The training dataset for the triangle enhancements are generated by using a set of hadrons satisfying the required mass condition. The training line shapes for the pole-based classifications are generated…
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Taxonomy
TopicsQuantum Chromodynamics and Particle Interactions
