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Analysis of FK/FPi with MDWF on gradient-flowed HISQ results from CalLat

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project_fkfpi

This repo performs the chiral, continuum, infinite volume extrapolation of the FK/Fpi results generated by CalLat using MDWF valence fermions solved on gradient-flowed gauge ensembles with Nf=2+1+1 dynamical HISQ fermions.

While most of the code in master branch was written by André Walker-Loud (walkloud), the design of the code is from Ben Hoerz (ebatz). Nolan Miller (millernb) wrote a completely independent fitting code (nolan branch) and the two were cross checked. The code was derived from an original implementation by Jason Chang (cchang5).

After Installation, in principle, the only file the user should have to modify is input_params.py where many options are controlled, such as what fit model to use, which data to include, whether to make plots, whether to save and use fits, etc. To run the fit, simply run

python fit_fkfpi.py

The loop over various choices of the fit "model" is controlled with the switches['sys'] dictionary. For example, to loop over the choice of using F=Fpi, F=FK or F^2 = Fpi FK, set

switches['sys']['Lam_chi'] = True

Other options for printing the lattice results in LaTeX cp/paste format, printing figures etc. are also controlled with the switches dictionary.

Further down the input_params.py is the setting of the priors used in the extrapolation analysis, followed by a definition of the physical point, phys_point for setting the final extrapolation value. Also, values of the masses and decay constants and LECs are set to perform a fit-function check with check_fit.

NOTE: if yourlsqfit` is not compiled against GSL, you have to set

switches['scipy']            = True

We installed with GSL support by specifying

pip install --no-cache-dir --global-option=build_ext --global-option="-lgsl" --global-option="-I/usr/local/include/gsl" --global-option="-L/usr/local/lib" gvar==11.2
pip install --no-cache-dir --global-option=build_ext --global-option="-lgsl" --global-option="-I/usr/local/include/gsl" --global-option="-L/usr/local/lib" lsqfit==11.5.1

Installation

Required Packages

  • standard scientific Python libraries (numpy, scipy, PyTables, matplotlib, yaml, functools)
  • gcc/clang, we used
Apple clang version 11.0.0 (clang-1100.0.33.12)
Target: x86_64-apple-darwin19.0.0

Installing

From the root directory of project_fkfpi:

conda install -c conda-forge pybind11

  • Download and compile CHIRON. We used chiron.v0.54.tar.gz

    • Follow the instructions in py_chiron/README for editing the Makefile of chiron,

    • then

    cd chiron.v0.54
    make [-j N]
    cd ../py_chiron
    
  • Build the Python Binding to CHIRON

    • edit the build_<VERION>.sh script to point to necessary directories and then
    ./build_<VERSION>.sh
    

    If successful, this will have created a library such as chiron.cpython-37m-darwin.so.

  • Test the binding

./test_binding.py
FF(0.7**2) =  0.01841657802571616
FF(0.2**2) =  0.5084416863714661
FF(gvar(0.2,0.1)**2) =  0.51(20)
FF(gvar(0.8,0.1)**2) =  0.0082(69)

Copyright Notice

project_fkfpi Copyright (c) 2020, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved. If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at IPO@lbl.gov.

NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so.

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Analysis of FK/FPi with MDWF on gradient-flowed HISQ results from CalLat

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