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PreprintCC BY 4.0HAL

OperatorToC++: Transpiling Matched EFT Coefficients to Low-Level Routines

  • Sabine Kraml
  • Andre Lessa
  • Suraj Prakash
  • Felix Wilsch
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Abstract

In recent years, significant progress has been made in the development of automated tools that match the parameters of new physics models and the appropriate low-energy Effective Field Theories. This work introduces an extensible, hybrid tool, OperatorToC++, that combines the strengths of Mathematica and C++ to facilitate the next steps beyond the matching. OperatorToC++ efficiently tackles the complexities within the analytical matched expressions such as intricate loop-functions and lengthy sums and products involving tensor objects. It then translates and bundles the results into C++ classes and functions which provide a convenient platform for further numerical analyses. Finally, it offers the possibility of calling the compiled Wilson coefficient methods as Python functions, thus enabling to link them with the vast Python library ecosystem for High Energy Physics workflows.

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Cite

BibTeXRIS
BibTeX
@misc{kraml2026operatortoc,
  title = {{OperatorToC++: Transpiling Matched EFT Coefficients to Low-Level Routines}},
  author = {Sabine Kraml and Andre Lessa and Suraj Prakash and Felix Wilsch},
  howpublished = {HAL},
  year = {2026},
  month = aug,
  doi = {10.48550/arxiv.2608.26254},
  eprint = {2608.26254},
  archivePrefix = {arXiv},
  url = {https://hal.science/hal-05741417v1},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - OperatorToC++: Transpiling Matched EFT Coefficients to Low-Level Routines
AU  - Sabine Kraml
AU  - Andre Lessa
AU  - Suraj Prakash
AU  - Felix Wilsch
PY  - 2026
DA  - 2026/08/26
PB  - HAL
DO  - 10.48550/arxiv.2608.26254
UR  - https://hal.science/hal-05741417v1
AB  - In recent years, significant progress has been made in the development of automated tools that match the parameters of new physics models and the appropriate low-energy Effective Field Theories. This work introduces an extensible, hybrid tool, OperatorToC++, that combines the strengths of Mathematica and C++ to facilitate the next steps beyond the matching. OperatorToC++ efficiently tackles the complexities within the analytical matched expressions such as intricate loop-functions and lengthy sums and products involving tensor objects. It then translates and bundles the results into C++ classes and functions which provide a convenient platform for further numerical analyses. Finally, it offers the possibility of calling the compiled Wilson coefficient methods as Python functions, thus enabling to link them with the vast Python library ecosystem for High Energy Physics workflows.
ER  -