RSOM applies dictionary learning to discover a sparse dictionary that conditions the analytic continuation inverse problem, yielding competitive results on synthetic tests and finite-temperature electron gas QMC data.
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A Luttinger-Ward analysis of the slave-rotor formalism is developed to compute Green's functions and thermodynamic properties in the U(1) spin liquid phase of the Hubbard model beyond mean-field.
Radiative corrections applied to MINERvA antineutrino data yield updated values for the nucleon axial-vector form factor G_A and axial radius.
The paper reviews the use of the imaginary-time correlation function to extract temperature, normalization, and Rayleigh weight from XRTS spectra without model dependence.
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Discovering a well-conditioned analytic continuation problem via dictionary learning
RSOM applies dictionary learning to discover a sparse dictionary that conditions the analytic continuation inverse problem, yielding competitive results on synthetic tests and finite-temperature electron gas QMC data.