$\texttt{SpaceMath v.2.0}$ 结合机器学习。一个用于超越标准模型参数空间搜索的 $\texttt{Mathematica}$ 软件包
$\texttt{SpaceMath v.2.0}$ with Machine Learning. A $\texttt{Mathematica}$ package for Beyond the Standard Model parameter space searches
AI总结：
结合机器学习的 $\texttt{SpaceMath v.2.0}$ 软件包扩展了超越标准模型参数空间搜索功能，新增了LHC希格斯数据和味改变中性流过程，并通过机器学习生成基准点以辅助物理观测量的数值计算。
摘要：
结合机器学习的 $\texttt{SpaceMath v.2.0}$ 是先前版本的扩展，我们在此实现了与 LHC Higgs 玻色子数据相关的观测值及其对于 High Luminosity 和 High Energy Large Hadron Collider 的预测。在此版本中，我们实现了树级和单圈级下涉及味改变中性流的物理过程，即：i) 辐射衰变 $\ell_i\to\ell_j γ$，ii) $\ell_i\to \ell_j \ell_k \bar{\ell}_k$ 衰变（$\ell_i=τ,\\,μ$，$\ell_{j,\\,k}=μ,\\,e$，且 $\ell_i \neq\ell_j \neq\ell_k$）以及 iii) 缪子的反常磁偶极矩 $δa_μ$。$\texttt{SpaceMath v.2.0}$ 能够利用上述过程，在友好的界面和直观的环境中，为具有实单态、复单态、实二重态和复二重态的模型寻找自由参数的允许区域，用户在其中以符号方式输入耦合，设置参数并以传统方式执行 $\texttt{Mathematica}$。作为结果，系统会生成与实验数据相符的数值表格和区域图。我们展示了使用 $\texttt{SpaceMath v.2.0}$ 逐步分析自由的三型双希格斯二重态模型参数空间的示例，以便让新用户快速高效地上手。最后，我们在该版本的 $\texttt{SpaceMath}$ 中实现了机器学习算法，以生成特定的基准点，用于物理观测量计算的数值评估。
英文摘要：
\texttt{SpaceMath v.2.0} with Machine Learning is an extension of the previous version which we implement observables related with LHC Higgs boson data and their projections for the High Luminosity and High Energy Large Hadron Collider. In this version we implemented processes with Flavor-Changing Neutral Currents at tree and one-loop level, namely, i) Radiative decays $\ell_i\to\ell_j γ$, ii) $\ell_i\to \ell_j \ell_k \bar{\ell}_k$ decays ($\ell_i=τ,\,μ$, $\ell_{j,\,k}=μ,\,e$, with $\ell_i \neq\ell_j \neq\ell_k$) and iii) anomalous magnetic dipole moment of the muon $δa_μ$. \texttt{SpaceMath v.2.0} is able to find allowed regions for free parameters of models with both real and complex singlets and real and complex doublets using the processes previously mentioned within a friendly interface and an intuitive environment in which the user enters the couplings symbolically, sets parameters and execute \texttt{Mathematica} in the traditional way. As result, both tables as plots with values and areas agree with experimental data are generated. We present examples using \texttt{SpaceMath v.2.0} to analyze the free \textit{Two-Higgs Doublet Model of type III} parameter space, step by step, in order to start new users in a fast and efficient way. Finally, we have implemented in this version of \texttt{SpaceMath} algorithms of Machine Learning to generate specific Benchmark Points to be used directly in numerical evaluations of calculations of physical observables.
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。
