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

Junya Otsuki, Masayuki Ohzeki, Hiroshi Shinaoka, Kazuyoshi Yoshimi

研究成果: Article査読

47 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号061302
ジャーナルPhysical Review E
95
6
DOI
出版ステータスPublished - 2017 6月 21

ASJC Scopus subject areas

  • 統計物理学および非線形物理学
  • 統計学および確率
  • 凝縮系物理学

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