论文标题
人工原始模型：从简化模型结果中建立下一个标准模型的前体
Artificial Proto-Modelling: Building Precursors of a Next Standard Model from Simplified Model Results
论文作者
论文摘要
我们提出了一种新型算法，以确定已发表的LHC结果中新物理学的潜在分散信号。它采用随机步行算法来引入称为“原型模型”的新粒子集，这些粒子是根据Atlas和CMS（利用Smodels软件框架）对简化模型结果进行了测试的。组合算法确定了最大程度地违反SM假设的一组分析和/或信号区域，同时与数据库中的全部LHC约束保持兼容。通过在Smodels数据库中运行实验结果来证明我们的方法，我们发现当前最佳的原始模型是顶级伙伴，浅质夸克伙伴和最轻的中性新粒子，分别为1.2 TEV，700 GEV和160 GEV的次数。 SM假设的相应全局P值约为0.19；通过施工，不适用任何位置效果。
We present a novel algorithm to identify potential dispersed signals of new physics in the slew of published LHC results. It employs a random walk algorithm to introduce sets of new particles, dubbed "proto-models", which are tested against simplified-model results from ATLAS and CMS (exploiting the SModelS software framework). A combinatorial algorithm identifies the set of analyses and/or signal regions that maximally violates the SM hypothesis, while remaining compatible with the entirety of LHC constraints in our database. Demonstrating our method by running over the experimental results in the SModelS database, we find as currently best-performing proto-model a top partner, a light-flavor quark partner, and a lightest neutral new particle with masses of the order of 1.2 TeV, 700 GeV and 160 GeV, respectively. The corresponding global p-value for the SM hypothesis is approximately 0.19; by construction no look-elsewhere effect applies.
