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 review of thermal modifications to light and heavy hadron properties via imaginary-time formalism, effective field theories, unitarized approaches, and lattice QCD, with links to heavy-ion phenomenology.
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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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Hadron properties at finite temperature
A review of thermal modifications to light and heavy hadron properties via imaginary-time formalism, effective field theories, unitarized approaches, and lattice QCD, with links to heavy-ion phenomenology.