论文标题
通过模型 - 敏捷的机器学习来增强单喷射搜索
Boosting mono-jet searches with model-agnostic machine learning
论文作者
论文摘要
我们展示了弱监督的机器学习如何改善具有异常喷射动力学的新物理模型的LHC单流喷射搜索的敏感性。没有标签的分类（CWOLA）方法用于从低级检测器信息中提取所有可用的信息，而无需参考特定的新物理模型。对于强烈交互的暗物质模型的示例，我们采用模拟数据来表明可以大大提高现有通用搜索的发现潜力。
We show how weakly supervised machine learning can improve the sensitivity of LHC mono-jet searches to new physics models with anomalous jet dynamics. The Classification Without Labels (CWoLa) method is used to extract all the information available from low-level detector information without any reference to specific new physics models. For the example of a strongly interacting dark matter model, we employ simulated data to show that the discovery potential of an existing generic search can be boosted considerably.
