We investigate enhancing the sensitivity of new physics searches at the LHC by machine learning in the case of background dominance and a high degree of overlap between the observables for signal and background. We use two different models, XGBoost and a deep neural network, to exploit correlations between observables and compare this approach to the traditional cut-and-count method. We consider different methods to analyze the models' output, finding that a template fit generally performs better than a simple cut. By means of a Shapley decomposition, we gain additional insight into the relationship between event kinematics and the machine learning model output. We consider a supersymmetric scenario with a metastable sneutrino as a concrete example, but the methodology can be applied to a much wider class of models.

翻译：我们研究在背景主导且信号与背景可观测量高度重叠的情况下，利用机器学习提升LHC上新物理搜索的灵敏度。我们采用XGBoost和深度神经网络两种不同模型来挖掘可观测量之间的关联性，并将该方法与传统计数截断法进行比较。通过分析模型输出的不同策略，我们发现模板拟合通常优于简单截断。结合沙普利分解，我们进一步揭示了事件运动学与机器学习模型输出之间的内在联系。本文以亚稳态中微子超对称场景为具体案例，但该方法论可推广至更广泛的模型类别。