AI 中文总结
该研究推出混合工具 OperatorToC++，结合 Mathematica 与 C++ 优势处理新物理模型匹配后的复杂表达式，转译为 C++ 代码并支持 Python 调用，以推进高能物理数值分析。
AI 中文摘要
近年来，自动工具在匹配新物理模型参数与对应低能有效场论（EFT）参数方面取得了显著进展。本文介绍一款可扩展的混合工具 OperatorToC++，它结合 Mathematica 与 C++ 的优势，助力匹配后的后续工作。OperatorToC++ 可高效处理解析匹配表达式中的复杂内容，如复杂的圈函数、涉及张量对象的冗长求和与乘积；随后将结果转译为 C++ 类与函数，为进一步数值分析提供便利平台；最后还支持将编译后的威尔逊系数方法作为 Python 函数调用，从而可将其与高能物理工作流中庞大的 Python 库生态系统相连接。
英文摘要
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.
Comments27 pages, 4 figures, 1 table, 5 code listings
