Sarah
Sarah
摘要：
SARAH是一个用于构建和分析超对称模型的Mathematica软件包。SARAH只需要规范结构、粒子含量和超势就能产生关于模型规范本征态的所有信息。规范对称性的破缺和粒子的混合可以很容易地在第二步中完成，整个拉格朗日量可以自动导出。在此基础上，由SARAH导出了R_Xi规范中的规范固定项，并计算了相应的鬼相互作用。利用这些信息，SARAH可以计算出给定模型的所有质量矩阵、蝌蚪方程和树级顶点。此外，还可计算出所有参数的单圈和双圈重整化群方程的表达式，并可自动计算对自能和蝌蚪的单圈修正。SARAH可以将所有关于模型的信息写入LaTeX文件，或者为FeynArts/FormCalc，WHIZARD/OMEGA和CalcHep/CompHep创建模型文件，这些文件也可以用于使用MicrOmegas的暗物质研究，以及MadGraph 5支持的UFO格式。从版本3开始，SARAH也是第一个可用的光谱发生器发生器：基于推导的分析表达式，它为SPheno创建源代码，以高精度计算质谱以及超对称性衰变。这样，就可以在SPheno中实现新的模型，而不需要手工编写任何Fortran代码。SARAH的公共版本中已经包含了许多MSSM之外的模型，并且新模型的实现简单明了。
英文摘要：
SARAH is a Mathematica package for building and analyzing supersymmetric models. SARAH just needs the gauge structure, particle content and superpotential to produce all information about the gauge eigenstates of a model. Breaking of gauge symmetries and mixings of particles can easily be in a second step and entire Lagrangian is derived automatically. Also the gauge fixing terms are derived by SARAH in R_Xi gauge, and the corresponding ghost interactions are calculated. Using this information, SARAH can calculate the all mass matrices, tadpole equations and vertices at tree-level for the given model. In addition, the expressions for the 1- and 2-loop renormalization group equations of all parameters can be calculated and an automatic calculation for the 1-loop corrections to self energies and the tadpoles are possible. SARAH can write all information about the model to LaTeX files, or create a model files for FeynArts/FormCalc, WHIZARD/OMEGA and CalcHep/CompHep, which can also be used for dark matter studies using MicrOmegas, and in the UFO format which is supported by MadGraph 5. Beginning with version 3, SARAH is also the first available spectrum-generator-generator: based on the derived, analytical expression it creates source code for SPheno to calculate the mass spectrum as well the SUSY decays with high precision. In that way, it is possible to implement new models in SPheno without the need to write any Fortran code by hand. Already many models beyond the MSSM are included in the public version of SARAH and the implementation of new models is easy and straightforward.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
