A Model-Independent Boosted Decision Tree Approach to Event Classification in MicroBooNE

MicroBooNE is a 170-ton Liquid argon time projection chamber (LArTPC) detector located in the Booster Neutrino Beam (BNB) beamline at Fermilab. One of the primary goals of MicroBooNE is investigating the anomalous excess of low energy electron neutrino like signals observed by other short-baseline experiments over the past two decades. With its LArTPC technology, MicroBooNE can probe and distinguish electromagnetic showers originating from both electron neutrino interactions as well as background photons, a vast improvement over prior experiments. Recent MicroBooNE efforts have focused on specific signal channels, looking for signals originating from one model or a particular class of models. In this talk, we present novel work towards the next generation photon and electron-positron separation techniques, using a modern GPU enhanced Boosted Decision Tree (BDT) framework utilizing XGBoost (Extreme Gradient Boosting) and other powerful machine learning libraries to train a single model-independent BDT to separate out various important background and signal categories.