  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.
