Scale setting with $m_\Omega$

git repo

Python code for our scale setting analysis.

Scale setting with mΩ and w0

This repository performs the chiral, continuum and infinite volume extrapolations of w_0 m_Omega to perform a scale setting on the MDWF on gradient-flowed HISQ action. The present results accompany the scale setting publication available at arXiv:2011.12166.

The analysis was performed by Nolan Miller (millerb) with the master branch, and Logan Carpenter ( loganofcarpenter) with cross checks by André Walker-Loud (walkloud) on the andre branch.

The raw correlation functions can be found here and the bootstrap results for the ground state masses and values of Fpi are contained in the file data/omega_pi_k_spec.h5.

How to use

To generate the extrapolation and interpolation results from the paper, run python -c [name]. This will automatically create the folder /results/[name]/ . A summary of the results is given inside /results/[name]/ Extra options can be viewed by running python --help, which is given below for convenience.

usage: [-h] [-c COLLECTION_NAME] [-m MODELS [MODELS ...]] [-ex EXCLUDED_ENSEMBLES [EXCLUDED_ENSEMBLES ...]] [-em {all,order,disc,alphas}] [-df DATA_FILE] [-re] [-mc] [-nf] [-na] [-d]

Perform scale setting

optional arguments:
  -h, --help            show this help message and exit
                        fit with priors and models specified in /results/[collection]/{prior.yaml,settings.yaml} and save results
  -m MODELS [MODELS ...], --models MODELS [MODELS ...]
                        fit specified models
                        exclude specified ensembles from fit
  -em {all,order,disc,alphas}, --empirical_priors {all,order,disc,alphas}
                        determine empirical priors for models
  -df DATA_FILE, --data_file DATA_FILE
                        fit with specified h5 file
  -re, --reweight       use charm reweightings on a06m310L
  -mc, --milc           use milc's determinations of a/w0
  -nf, --no_fit         do not fit models
  -na, --no_average     do not average models
  -d, --default         use default priors; defaults to using optimized priors if present, otherwise default priors

To fine-tune the results, either re-run the fits using the options above or by modifying /results/[name]/settings.yaml. Similarly, the fits can be constructed with different priors by editing /results/[name]/priors.yaml and re-running python -c [name].

In addition to this library, this repo contains Juypyter notebooks. The fit for a single model can be explored in /notebooks/fit_model.ipynb. The model average is provided in /notebooks/average_models.ipynb. Some miscellaneous drudgery (eg, the paper’s sensitivity figure) is available in /notebooks/bespoke_plots.ipynb.


This work makes extensive use of Peter Lepage’s Python modules gvar and lsqfit, which are used to construct the fits and model average. Further, the settings and priors are primarily tweaked by the accompanying yaml files loaded via PyYAML.


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