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A Full-Fledged Analytic Continuation Toolkit in Julia

Home Page: https://huangli712.github.io/projects/acflow/index.html

License: GNU General Public License v3.0

Julia 100.00%
maximum-entropy-method stochastic-optimization-method stochastic-pole-expansion analytic-continuation-problem nevanlinna-analytical-continuation stochastic-analytic-continuation high-energy-physics quantum-many-body-physics many-body-physics

acflow's Introduction

ACFlow

The ACFlow toolkit implements several state-of-the-art analytic continuation methods, including the maximum entropy method, the stochastic analytic continuation, the stochastic optimization method, the stochastic pole expansion, the Nevanlinna analytical continuation, and the barycentric rational function method. It can be used to convert the single-particle or two-particle correlation functions, which are generated by finite-temperature quantum many-body simulations, from imaginary time or imaginary frequency axis to real axis.

This toolkit is currently under developement. PLEASE USE IT AT YOUR OWN RISK!

Version

v2.0.0-devel.240808

License

GNU General Public License Version 3

Documentation

See acflow/docs.

acflow's People

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acflow's Issues

A question about the input standard deviation

Thank you for providing the excellent package and documents for analytical continuation. I am new to DQMC and analytical continuation, and I have noticed that there is a mix-up in the terminology used in the codes regarding ”standard deviation“ and ”error“. This has caused confusion for me, particularly when it comes to the input "standard deviation" for ACFlow. I would like to clarify whether it should be the actual standard deviation $\sigma=\sqrt{\frac{\sum_{i=1}^n \left(x_i-\bar{x}\right)^2}{n}}$, or the standard error $\sigma_n=\frac{\sigma}{\sqrt{n}}$ (where $n$ represents the number of samples). For my data, the standard deviations maybe large while standard errors are smaller. And I am afraid that standard deviations are too noisy.

Requesting ability to turn off file output

First off, ACFlow is looking great so far. Great work!

I am looking to incorporate ACFlow into an in-house Julia DQMC project which is in development. Because everything will be in Julia we have no need for the default ACFlow file output. Could you add a variable to disable file output? I think adding it in the [BASE] section of the toml (or in our case B dictionary) would probably be the easiest way to handle it without making the code any more complicated for the non-Julia users.

Thanks,
James Neuhaus
Steve Johnston Group

StochOM ktype=bsymm, grid=btime giving NaN

When running ACFlow (current master branch) the stochastic optimization method for symmetric time kernels I am getting NaN for the delta. Below is the output.

I've attached an example script with input data. I also run MaxEnt which converges well.

Thanks.

[ StochOM ]
Create infrastructure for Monte Carlo sampling
Postprocess input data: 201 points
Build grid for input data: 201 points
Build mesh for spectrum: 1000 points
try ->      1 (    25) Δ ->      NaN 
try ->      2 (    25) Δ ->      NaN 
try ->      3 (    25) Δ ->      NaN 
try ->      4 (    25) Δ ->      NaN 
try ->      5 (    25) Δ ->      NaN 
try ->      6 (    25) Δ ->      NaN 
try ->      7 (    25) Δ ->      NaN 
try ->      8 (    25) Δ ->      NaN 
try ->      9 (    25) Δ ->      NaN 
try ->     10 (    25) Δ ->      NaN 
try ->     11 (    25) Δ ->      NaN 
try ->     12 (    25) Δ ->      NaN 
try ->     13 (    25) Δ ->      NaN 
try ->     14 (    25) Δ ->      NaN 
try ->     15 (    25) Δ ->      NaN 
try ->     16 (    25) Δ ->      NaN 
try ->     17 (    25) Δ ->      NaN 
try ->     18 (    25) Δ ->      NaN 
try ->     19 (    25) Δ ->      NaN 
try ->     20 (    25) Δ ->      NaN 
try ->     21 (    25) Δ ->      NaN 
try ->     22 (    25) Δ ->      NaN 
try ->     23 (    25) Δ ->      NaN 
try ->     24 (    25) Δ ->      NaN 
try ->     25 (    25) Δ ->      NaN 
Median χ² :              NaN Accepted configurations :     0

som_check.zip

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