A hierarchy of semidefinite programs provides rigorous bounds on spectral density functionals from Monte Carlo data subject to reflection positivity, converging to statistical error limits.
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Deep learning extracts a unified in-medium heavy quark potential from multi-energy bottomonium data, finding the real part close to vacuum Cornell form with weak screening while the imaginary part dominates suppression.
Radiative corrections applied to MINERvA antineutrino data yield updated values for the nucleon axial-vector form factor G_A and axial radius.
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Certified spectral functions from lattice Monte Carlo data
A hierarchy of semidefinite programs provides rigorous bounds on spectral density functionals from Monte Carlo data subject to reflection positivity, converging to statistical error limits.
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Unified Extraction of In-Medium Heavy Quark Potentials from RHIC to LHC Energies via Deep Learning
Deep learning extracts a unified in-medium heavy quark potential from multi-energy bottomonium data, finding the real part close to vacuum Cornell form with weak screening while the imaginary part dominates suppression.
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Nucleon axial-vector form factor and radius from radiatively-corrected antineutrino scattering data
Radiative corrections applied to MINERvA antineutrino data yield updated values for the nucleon axial-vector form factor G_A and axial radius.