Neural network learns confining potentials and dilaton profile in holographic QCD from meson spectra, predicting steeper IR dilaton and pion masses with good accuracy.
Geometric approach to condensates in holographic QCD
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abstract
An SU(Nf)xSU(Nf) Yang-Mills theory on an extra-dimensional interval is considered, with appropriate symmetry-breaking boundary conditions on the IR brane. UV-brane to UV-brane correlators at high energies are compared with the OPE of two-point functions of QCD quark currents. Condensates correspond to departure from AdS of the (different) metrics felt by vector and axial combinations, away from the UV brane. Their effect on hadronic observables is studied: the extracted condensates agree with the signs and orders of magnitude expected from QCD.
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Learning holographic QCD with unflavoured meson spectra
Neural network learns confining potentials and dilaton profile in holographic QCD from meson spectra, predicting steeper IR dilaton and pion masses with good accuracy.