Neural-network quantum states applied to HAL QCD meson-nucleon potentials predict bound states for phi at A>=2, J/psi at A>=4, and eta_c at A>=6, with binding energies from tens of MeV to sub-MeV scales.
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A physics-guided neural network embedding AdS5 Dirac equation and holographic Pomeron fits SLAC proton F2 data with chi-squared per degree of freedom of 0.91 and identifies a kinematic crossover at x approximately 0.19 while recovering Pomeron intercept of 1.0786.
A new two-step Gaussian expansion method enables high-precision calculation of fine structure in negative-parity singly heavy baryons via the relativized quark model, reproducing data to <5 MeV average deviation.
citing papers explorer
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Meson-Nucleus Bound States with Neural-Network Quantum States
Neural-network quantum states applied to HAL QCD meson-nucleon potentials predict bound states for phi at A>=2, J/psi at A>=4, and eta_c at A>=6, with binding energies from tens of MeV to sub-MeV scales.
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Probing Proton Structure via Physics-Guided Neural Networks in Holographic QCD
A physics-guided neural network embedding AdS5 Dirac equation and holographic Pomeron fits SLAC proton F2 data with chi-squared per degree of freedom of 0.91 and identifies a kinematic crossover at x approximately 0.19 while recovering Pomeron intercept of 1.0786.
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Spin-dependent interactions and fine structure in the negative-parity singly heavy baryons
A new two-step Gaussian expansion method enables high-precision calculation of fine structure in negative-parity singly heavy baryons via the relativized quark model, reproducing data to <5 MeV average deviation.
- All-charm tetraquarks at hadron colliders: A high-precision fragmentation perspective