人工原型建模：从简化模型结果构建下一个标准模型的先驱
Artificial Proto-Modelling: Building Precursors of a Next Standard Model from Simplified Model Results
摘要：
我们提出了一种新的算法来识别潜在的分散信号的新物理在一系列已发表的LHC结果。它采用随机游走算法来引入新的粒子集，称为“原型模型”，这些粒子集是针对ATLAS和CMS（利用SModelS软件框架）的简化模型结果进行测试的。一个组合算法确定的一组分析和/或信号区域，最大限度地违反了SM假设，同时保持兼容的整个LHC的约束在我们的数据库中。通过运行SModelS数据库中的实验结果来演示我们的方法，我们发现目前表现最好的原模型是一个顶级伙伴，一个轻味夸克伙伴和一个最轻的中性新粒子，质量分别为1.2 TeV，700 GeV和160 GeV。SM假设的相应全局p值约为0.19;通过构建，不适用其他地方效应。
英文摘要：
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.
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。
