论文标题
$ \ texttt {spacemath v.2.0} $带机器学习。 $ \ texttt {Mathematica} $软件包，超出标准模型参数空间搜索
$\texttt{SpaceMath v.2.0}$ with Machine Learning. A $\texttt{Mathematica}$ package for Beyond the Standard Model parameter space searches
论文作者
论文摘要
\ texttt {spacemath v.2.0}借助机器学习是先前版本的扩展，我们实现了与LHC Higgs Boson数据相关的可观察物，及其对高亮度和高能大型强子对撞机的预测。在此版本中，我们在树和一环级别上以变化的中性电流实现了过程，即，i）辐射衰减$ \ ell_i \ to \ell_jγ$，ii）$ \ ell_i \ to \ ell_j \ ell_j \ ell_j \ ell_k \ ell_k \ ell_k \ bar \ bar {\ ell} _k $ $ $ depays（$ ell_ $ deSays（ $ \ ell_ {j，\，k} =μ，\，e $，带有$ \ ell_i \ neq \ ell_j \ neq \ neq \ ell_k $）和iii）Muon $ΔA__$的异常磁性偶极力矩。 \ texttt {spacemath v.2.0}能够使用友好界面和用户象征性地进入耦合的友好界面和直观环境中提到的过程，找到具有真实和复杂单元的免费参数，并具有真实和复杂的单元以及真实且复杂的双线，并设置了\ texttt coments and eccute \ texttttttt {Mathemememagatica} in norkation。结果，两个表作为具有值的图和区域与实验数据一致的表。我们使用\ texttt {spacemath v.2.0}介绍示例，分析免费的\ textit {type III} type iii}参数空间的doublet模型，逐步，以快速有效地启动新用户。最后，我们已经在此版本的\ texttt {spacemath}算法中实现了机器学习算法，以生成要直接用于物理可观察物的计算数值评估中的特定基准点。
\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.
