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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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.
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Model-free interpretation of X-ray Thomson scattering measurements
The paper reviews the use of the imaginary-time correlation function to extract temperature, normalization, and Rayleigh weight from XRTS spectra without model dependence.