Finite-temperature Yang-Mills theories with the density of states method: towards the continuum limit - Analysis workflow

The workflow in this repository performs the analyses presented in the paper Finite-temperature Yang-Mills theories with the density of states method: towards the continuum limit [2509.19009].

Requirements

• Conda, for example, installed from Miniforge
• Snakemake, which may be installed using Conda

Setup

1. Install the dependencies above.

2. Clone this repository including submodules (or download its Zenodo release and unzip it) and cd into it:

   git clone --recurse-submodules https://github.com/telos-collaboration/llr_analysis
   cd llr_analysis

3. The raw data and metadata can be downloaded from Zenodo . Download metadata.zip, raw_data.zip and decompress_raw_data.sh from Zenodo and decompress the archives in this directory by invoking decompress_raw_data.sh. On slow and/or unstable connections consider using wget -c to download large files. After decompression the raw data takes up roughly 90GB of space.

Running the workflow

The workflow is run using Snakemake:

snakemake --cores 1 --use-conda

where the number 1 may be replaced by the number of CPU cores you wish to allocate to the computation.

Snakemake will automatically download and install all required Python packages. This requires an Internet connection; if you are running in an HPC environment where you would need to run the workflow without Internet access, details on how to preinstall the environment can be found in the Snakemake documentation.

Using all 16 cores on an AMD 5950x CPU the analysis takes roughly 6 minutes.

time snakemake --use-conda --cores 16 --forceall
real    6m11.190s
user    46m39.241s
sys     2m39.236s

Output

Output plots, tables, and definitions are placed in the assets/plots, assets/tables, and assets/definitions directories.

Output data assets are placed into the data_assets directory.

Intermediary data are placed in the intermediary_data directory.

Reusability

This workflow is relatively tailored to the data which it was originally written to analyse. Additional ensembles may be added to the analysis by adding relevant files to the raw_data directory, and adding corresponding entries to the files in the metadata directory. However, extending the analysis in this way has not been as fully tested as the rest of the workflow, and is not guaranteed to be trivial for someone not already familiar with the code.