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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The OMG3Q1.1 framework delivers the first uncertainty-quantified set of fragmentation functions for all-heavy Ω_{3Q} baryons via diquark-inspired inputs, HF-NRevo evolution, and replica-based error estimation.
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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Toward Precision Fragmentation of $\Omega_{3Q}$ Baryons: The OMG3Q1.1 Framework
The OMG3Q1.1 framework delivers the first uncertainty-quantified set of fragmentation functions for all-heavy Ω_{3Q} baryons via diquark-inspired inputs, HF-NRevo evolution, and replica-based error estimation.