modern_nn_potentials

Accompanying works

This repository contains all code and data necessary to generate the results in Assessing Correlated Truncation Errors in Modern Nucleon-Nucleon Potentials (https://arxiv.org/abs/2402.13165) and Assessing Convergence Patterns Across Modern Nucleon-Nucleon Potentials (https://arxiv.org/abs/2508.17558).

Installation

• This project relies on python=3.8. It was not tested with different versions. To view the entire list of required packages, see environment.yml.
• Note that a LaTeX distribution (such as Miktex) is also necessary in order to render the figures.
• Clone the repository to your local machine.
• Once you have cd into this repo, create a virtual environment (assuming you have conda installed) via

conda env create -f environment.yaml

• Enter the virtual environment with conda activate modern-nn-potentials
• Install the cheftgp package in the repo root directory using pip install -e . (you only need the -e option if you intend to edit the source code in cheftgp/).

Contents

Beginning with order-by-order predictions of nucleon-nucleon (NN) scattering observables generated using modern NN potentials that are implementations of chiral effective field theory ($\chi$EFT), this library extracts dimensionless coefficients according to the BUQEYE model.

• Data for 12 potentials is found in observables_data.

From here, one can plot the resulting coefficients and test how well they are described as random draws from the same Gaussian process (GP) using statistical diagnostics.

• To generate the figures found in Assessing Correlated Truncation Errors in Modern Nucleon-Nucleon Potentials, run notebooks/figures1.ipynb.
• To generate the figures found in Assessing Convergence Patterns Across Modern Nucleon-Nucleon Potentials, run notebooks/figures2.ipynb and notebooks/warping_playground.ipynb.

Additionally, one can evaluate the posterior probability distribution functions for parameters of the theory such as the breakdown momentum scale $\Lambda_{b}$ and the soft scale $m_{\mathrm{eff}}$.

• To generate the values of these parameters found in Assessing Correlated Truncation Errors in Modern Nucleon-Nucleon Potentials's Table III, run scripts/posteriors_script_tab3_x.py (with x replacing the number of the row of interest in that table).
• To generate the values of these parameters found in Assessing Convergence Patterns Across Modern Nucleon-Nucleon Potentials's Table II, run scripts/posteriors_script_warped_mpifixed.py with the appropriate potential input in the code, and scripts/posteriors_script_scaledls_y.py (with y replacing the short name of the potential of interest) for the variable $m_{\mathrm{eff}}$ results.

For best results, run on a computing cluster and not a local machine.

Miscellaneous

Unit tests can be found in tests/unit_tests.ipynb.

Citing this work

Please cite this work as follows:

@misc{millican2024assessing,
      title={Assessing Correlated Truncation Errors in Modern Nucleon-Nucleon Potentials}, 
      author={P. J. Millican and R. J. Furnstahl and J. A. Melendez and D. R. Phillips and M. T. Pratola},
      year={2024},
      eprint={2402.13165},
      archivePrefix={arXiv},
      primaryClass={nucl-th}
}