Bayesian Analysis of Nuclear Dynamics

The BAND collaboration develops open-source statistical tools and software frameworks for uncertainty quantification in nuclear physics. Our goal is to produce forecasts for as-yet-unexplored nuclear environments — from neutron star interiors to the first seconds of the universe — by combining the predictive power of multiple theoretical models in a statistically rigorous way. BAND tools are built to be general, interoperable, and accessible to both domain experts and broader scientific communities.

BAND is supported by the NSF CSSI program under grant OAC-2004601. For more information, visit our website.

All packages are bundled together in the BAND Framework repository, with the latest release at v0.5.

BAND tools are deployed through accessible web platforms that bring uncertainty quantification to a broader audience:

• BMEX — The Bayesian Mass Explorer: an interactive web application for exploring and comparing theoretical nuclear mass predictions across the nuclear chart, powered by pyBMC.
• CANDL — The Current Archive of Nuclear Density of Levels: a searchable, interactive database of experimental nuclear level density datasets.

The BAND Framework repository is the best starting point. It includes all BAND packages, worked examples, and notebooks. Individual packages can also be installed independently — most are available via pip.

For a guided introduction to the methodology, see the BAND Manifesto in Journal of Physics G.

We welcome contributions to BAND software. All packages in the framework follow the BAND SDK policies, which ensure interoperability, documentation, and open licensing across the ecosystem. See the contribution guidelines for details on adding a new package.

If you use BAND software in your research, please cite the framework:

    @techreport{bandframework,
        title       = {{BANDFramework: An} Open-Source Framework for {Bayesian} Analysis of Nuclear Dynamics},
        author      = {Kyle Beyer and Landon Buskirk and Manuel Catacora Rios and Moses Y-H. Chan and Tyler H. Chang and Troy Dasher 
        and Richard James DeBoer and Christian Drischler and Richard J. Furnstahl and Pablo Giuliani and
        Kyle Godbey and Kevin Ingles and Sunil Jaiswal and An Le and Dananjaya Liyanage and Filomena M. Nunes
        and Daniel Odell and David O'Gara and Jared O'Neal and Daniel R. Phillips and Matthew Plumlee
        and Matthew T. Pratola and Scott Pratt and Oleh Savchuk and Alexandra C. Semposki and \"Ozge S\"urer and 
        Stefan M. Wild and John C. Yannotty},
        institution = {},
        number      = {Version 0.5.0},
        year        = {2025},
        url         = {https://github.com/bandframework/bandframework}
    }

If possible, please also cite the original BAND Framework paper:

    @article{Phillips:2020dmw,
        author = "Phillips, D. R. and others",
        title = "{Get on the BAND Wagon: A Bayesian Framework for Quantifying Model Uncertainties in Nuclear Dynamics}",
        eprint = "2012.07704",
        archivePrefix = "arXiv",
        primaryClass = "nucl-th",
        doi = "10.1088/1361-6471/abf1df",
        journal = "J. Phys. G",
        volume = "48",
        number = "7",
        pages = "072001",
        year = "2021"
    }

Individual packages have their own citation keys — please see each package's README.

BAND is committed to a welcoming, inclusive, and professional environment for all collaborators. Please read our full Code of Conduct before participating.

Questions? Open an issue in the relevant repository or reach out to the BAND team.