Citing FOXES

The method and results are described in our paper:

Goodwin, G. T., March, A. J., Biradar, J., Schirninger, C., Jarolim, R., Vourlidas, A., Sadykov, V. M., & Pratt, L. (2026). Improving Solar Flare Soft X-Ray Classification with FOXES: A Framework for Operational X-Ray Emission Synthesis. The Astrophysical Journal, 1003(1), 48. doi:10.3847/1538-4357/ae5e4a

If FOXES helps your work, please cite the paper:

@article{Goodwin_2026,
    title     = {Improving Solar Flare Soft X-Ray Classification with FOXES:
                 A Framework for Operational X-Ray Emission Synthesis},
    author    = {Goodwin, Griffin T. and March, Alison J. and Biradar, Jayant and
                 Schirninger, Christoph and Jarolim, Robert and Vourlidas, Angelos and
                 Sadykov, Viacheslav M. and Pratt, Lorien},
    journal   = {The Astrophysical Journal},
    volume    = {1003},
    number    = {1},
    pages     = {48},
    year      = {2026},
    publisher = {American Astronomical Society},
    doi       = {10.3847/1538-4357/ae5e4a}
}

If you use the code or data directly, you can cite the software as well:

@software{FOXES,
    title           = {{FOXES: A Framework For Operational X-ray Emission Synthesis}},
    institution     = {Frontier Development Lab (FDL)},
    repository-code = {https://github.com/griffin-goodwin/FOXES},
    version         = {v1.0},
    year            = {2026}
}

Team

Griffin Goodwin, Alison March, Jayant Biradar, Christoph Schirninger, Robert Jarolim, Angelos Vourlidas, Viacheslav Sadykov, and Lorien Pratt.

Acknowledgements

This work is a research product of Heliolab, an initiative of the Frontier Development Lab. FDL is a public–private partnership between NASA, Trillium Technologies, and commercial AI partners including Google Cloud and NVIDIA. Heliolab was designed, delivered, and managed by Trillium Technologies Inc., a research and development company focused on intelligent systems and collaborative communities for heliophysics, planetary stewardship, and space exploration. We gratefully acknowledge Google Cloud for extensive computational resources and NVIDIA Corporation.

This material is based upon work supported by NASA under award number No. 80GSFC23CA040. Any opinions, findings, conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of the National Aeronautics and Space Administration.