A kernel-derived orthogonal basis enables controlled approximation of spectral functions and low-energy transport coefficients from Euclidean correlators without priors.
Analytic Continuation of Quantum Monte Carlo Data: Optimal Stochastic Regularization Approach
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abstract
A new algorithm for analytic continuation of noisy quantum Monte Carlo (QMC) data from the Matsubara domain to real frequencies is proposed. Unlike the widely used maximum-entropy (MaxEnt) procedure, our method is linear with respect to input data and can therefore be applied to off-diagonal components of a thermal Green's function, or to a self-energy function. The latter possibility is used to analyze QMC results for the half-filled single-band Hubbard model on a Bethe lattice at a low temperature. Our method qualitatively resolves peaks near the inner edges of the Hubbard bands in the vicinity of a Mott transition, whereas a MaxEnt procedure does not. An existence of such structures has been clearly established before in a high-precision D-DMRG calculation by Karski et al. We also analyze a stability of the new method subject to changes of adjustable parameters.
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2026 1verdicts
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A kernel-derived orthogonal basis for spectral functions from Euclidean correlators
A kernel-derived orthogonal basis enables controlled approximation of spectral functions and low-energy transport coefficients from Euclidean correlators without priors.