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.
A stochastic approach to the reconstruction of spectral functions in lattice QCD
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abstract
We present a Stochastic Optimization Method (SOM) for the reconstruction of the spectral functions (SPFs) from Euclidean correlation functions. In this approach the SPF is parameterized as a sum of randomly distributed boxes. By varying the width, location and height of the boxes stochastically an optimal SPF can be obtained. Using this approach we reproduce mock SPFs fairly well, which contain sharp resonance peaks, transport peaks and continuum spectra. We also analyzed the charmonium correlators obtained from $N_{\tau}$=96, 48, 32 lattices using SOM and found similar conclusion on the dissociation temperatures of charmonium ground states as that obtained using the Maximum Entropy Method.
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2026 1verdicts
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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.