Journal Article

FZJ-2023-01245

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Connecting Tikhonov regularization to the maximum entropy method for the analytic continuation of quantum Monte Carlo data

Ghanem, K. ; Koch, E. (Corresponding author)FZJ*

2023
Inst. Woodbury, NY

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Please use a persistent id in citations: http://hdl.handle.net/2128/33957  doi:10.1103/PhysRevB.107.085129

Abstract: Analytic continuation is an essential step in extracting information about the dynamical properties of physical systems from quantum Monte Carlo (QMC) simulations. Different methods for analytic continuation have been proposed and are still being developed. This paper explores a regularization method based on the repeated application of Tikhonov regularization under the discrepancy principle. The method can be readily implemented in any linear algebra package and gives results surprisingly close to the maximum entropy method (MaxEnt). We analyze the method in detail and demonstrate its connection to MaxEnt. In addition, we provide a straightforward method for estimating the noise level of QMC data, which is helpful for practical applications of the discrepancy principle when the noise level is not known reliably.

Note: 12 pages, 10 figures

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2023

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Electronics and Telecommunications Collection ; Current Contents - Physical, Chemical and Earth Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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