Minimal pole representation and analytic continuation of matrix-valued correlation functions

The authors present here a method for analytically continuing matrix-valued correlation functions to the real axis, accurately capturing off-diagonal elements to enhance the interpretation of self-energies and susceptibilities. Based on a minimal information criterion, a selection of poles in the complex plane results in a universal noise-resistant approximation that converges reliably with increasing data precision. Comparisons with state-of-the-art techniques highlight its resolution and efficiency, establishing it as a significant advancement in computational quantum physics.

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Publication Details

Journal
Physical review. B./Physical review. B
Published
2024-12-12
DOI
https://doi.org/10.1103/physrevb.110.235131
Citations
11
Primary Topic
Matrix Theory and Algorithms
Type
article
Field-Weighted Citation Impact
3.98

Funders

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article

Minimal pole representation and analytic continuation of matrix-valued correlation functions

Emanuel Gull, L. Zhang, Yang Yu
11 citations
Physical review. B./Physical review. B
Matrix Theory and Algorithms
3.98
article

Minimal pole representation and analytic continuation of matrix-valued correlation functions

Emanuel Gull, L. Zhang, Yang Yu
article en
11 citations

Abstract

The authors present here a method for analytically continuing matrix-valued correlation functions to the real axis, accurately capturing off-diagonal elements to enhance the interpretation of self-energies and susceptibilities. Based on a minimal information criterion, a selection of poles in the complex plane results in a universal noise-resistant approximation that converges reliably with increasing data precision. Comparisons with state-of-the-art techniques highlight its resolution and efficiency, establishing it as a significant advancement in computational quantum physics.

Physical review. B./Physical review. BVol. 110(23)
University of Michigan (US)
National Science Foundation
Reduced inequalities
Openalex Percentile: Top 5%
Matrix Theory and Algorithms
3.98
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