Neural network analytic continuation for Monte Carlo: Improvement by statistical errors
Kai-Wei Sun(孙恺伟)1 and Fa Wang(王垡)1,2,†
1 International Center for Quantum Materials, School of Physics, Peking University, Beijing 100871, China; 2 Collaborative Innovation Center of Quantum Matter, Beijing 100871, China
Abstract This study explores the use of neural network-based analytic continuation to extract spectra from Monte Carlo data. We apply this technique to both synthetic and Monte Carlo-generated data. The training sets for neural networks are carefully synthesized without "data leakage". We find that the training set should match the input correlation functions in terms of statistical error properties, such as noise level, noise dependence on imaginary time, and imaginary time-displaced correlations. We have developed a systematic method to synthesize such training datasets. Our improved algorithm outperforms the widely used maximum entropy method in highly noisy situations. As an example, our method successfully extracted the dynamic structure factor of the spin-1/2 Heisenberg chain from quantum Monte Carlo simulations.
Fund: FW acknowledges support from the National Natural Science Foundation of China (Grant Nos. 12274004 and 11888101). Quantum Monte Carlo simulations are performed on TianHe-1A of National Supercomputer Center in Tianjin.
Corresponding Authors:
Fa Wang
E-mail: wangfa@pku.edu.cn
Cite this article:
Kai-Wei Sun(孙恺伟) and Fa Wang(王垡) Neural network analytic continuation for Monte Carlo: Improvement by statistical errors 2023 Chin. Phys. B 32 070705
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