Neural-network quantum states are used to compute spectra of fully-heavy multiquarks in a non-relativistic quark model, claiming to overcome dimensionality issues with superior accuracy over prior approximations.
Investigation of full-charm and full-bottom pentaquark states
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
hep-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Machine learning models trained on known hadron data and an extended Gürsey-Radicati mass formula predict masses for triply heavy baryons and numerous pentaquark states, agreeing with available data and forecasting unobserved states.
citing papers explorer
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Fully-heavy multiquarks in neural-network quantum states
Neural-network quantum states are used to compute spectra of fully-heavy multiquarks in a non-relativistic quark model, claiming to overcome dimensionality issues with superior accuracy over prior approximations.
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Comprehensive Mass Predictions: From Triply Heavy Baryons to Pentaquarks
Machine learning models trained on known hadron data and an extended Gürsey-Radicati mass formula predict masses for triply heavy baryons and numerous pentaquark states, agreeing with available data and forecasting unobserved states.