reflects an optimization to efficiently distinguish DY+jets events from other backgrounds and the signal. Figure \[fig:srvariables\_leptau\] shows the distributions of events passing the baseline selections in the [$\PGm\tauh$]{}final state in two of the BDT input variables that provide the highest discriminating power, and $\mT^{\text{tot}}$. The distributions observed in the [$\Pe\tauh$]{}final state are similar.

Since the signal kinematics depend on mass, we train BDTs for signals with masses of 100, 150, and 200. In all cases we use a mass of 1. As the results of the training depend critically on the number of input events, we relax the MVA-based isolation criteria and reduce the threshold for the to 20for the training sample in order to increase the number of training and test events. The “very tight" isolation and a threshold of 30for the are applied in the final analysis. For a given signal hypothesis, we choose the BDT trained with the same mass for models with masses of 100, 150, and 200, or the one that provides optimal sensitivity for models with other mass values. For signal models with masses of 90 and 125, we use the BDT trained for $m(\PSGt)=100\GeV$, while for those with a mass of 175, we 