Data-Driven cross checks for electron (anti)neutrino selection efficiency in NOvA

NOvA is a long-baseline neutrino oscillation experiment, designed to make measurements using νμ(νμ¯) disappearance and νe(νe¯) appearance. It consists of two functionally equivalent detectors and utilizes the Fermilab NuMI beam. NOvA uses a convolutional neural network for particle identification of νe events with a validation process that includes several data-driven techniques. Muon-Removed, Electron-Added studies involve selecting νμ charged current candidates from data and simulation and replacing the muon with a simulated electron of similar energy. For Muon-Removed Bremsstrahlung and Muon-Removed Decay-in-Flight studies we remove muonic hits from cosmic muons that have either experienced Bremsstrahlung radiation or decayed in flight, producing samples of pure electromagnetic showers. Each of these νe-like samples is then evaluated by our classifier to obtain selection efficiencies. Our most recent analysis showed good agreement in the νe selection efficiency using these techniques. These results and future improvements are presented here