Sparse modeling approach to analytical continuation of imaginary-time quantum Monte Carlo data

Junya Otsuki, Masayuki Ohzeki, Hiroshi Shinaoka, Kazuyoshi Yoshimi

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

A data-science approach to solving the ill-conditioned inverse problem for analytical continuation is proposed. The root of the problem lies in the fact that even tiny noise of imaginary-time input data has a serious impact on the inferred real-frequency spectra. By means of a modern regularization technique, we eliminate redundant degrees of freedom that essentially carry the noise, leaving only relevant information unaffected by the noise. The resultant spectrum is represented with minimal bases and thus a stable analytical continuation is achieved. This framework further provides a tool for analyzing to what extent the Monte Carlo data need to be accurate to resolve details of an expected spectral function.

Original languageEnglish
Article number061302
JournalPhysical Review E
Volume95
Issue number6
DOIs
Publication statusPublished - Jun 21 2017
Externally publishedYes

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

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