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MicroBooNE is a liquid argon time projection chamber (LArTPC) neutrinoexperiment that is currently running in the Booster Neutrino Beam at Fermilab.LArTPC technology allows for high-resolution, three-dimensional representationsof neutrino interactions. A wide variety of software tools for automatedreconstruction and selection of particle tracks in LArTPCs are actively beingdeveloped. Short, isolated proton tracks, the signal for low- momentum-transferneutral current (NC) elastic events, are easily hidden in a large cosmicbackground. Detecting these low-energy tracks will allow us to probeinteresting regions of the proton's spin structure. An effective method forselecting NC elastic events is to combine a highly efficient trackreconstruction algorithm to find all candidate tracks with highly accurateparticle identification using a machine learning algorithm. We present our workon particle track classification using gradient tree boosting software(XGBoost) and the performance on simulated neutrino data.

Automated proton track identification in MicroBooNE using gradient boosted decision trees