# SpM: Sparse modeling tool for analytic continuation of imaginary-time Green's function

## TL;DR

SpM is a new sparse modeling tool that improves the stability of analytic continuation of imaginary-time Green's functions in quantum Monte Carlo simulations, effectively handling noise and errors.

## Contribution

It introduces a regularization-based sparse modeling approach that automatically selects relevant bases, enhancing the stability of analytic continuation in noisy data.

## Key findings

- SpM achieves stable analytic continuation despite noise.
- The method automatically identifies noise-robust bases.
- Applications demonstrate improved spectral data extraction.

## Abstract

We present SpM, a sparse modeling tool for the analytic continuation of imaginary-time Green's function, licensed under GNU General Public License version 3. In quantum Monte Carlo simulation, dynamic physical quantities such as single-particle and magnetic excitation spectra can be obtained by applying analytic continuation to imaginary-time data. However, analytic continuation is an ill-conditioned inverse problem and thus sensitive to noise and statistical errors. SpM provides stable analytic continuation against noise by means of a modern regularization technique, which automatically selects bases that contain relevant information unaffected by noise. This paper details the use of this program and shows some applications.