  MicroBooNE is a liquid argon time projection chamber (LArTPC) neutrino
experiment that is currently running in the Booster Neutrino Beam at Fermilab.
LArTPC technology allows for high-resolution, three-dimensional representations
of neutrino interactions. A wide variety of software tools for automated
reconstruction and selection of particle tracks in LArTPCs are actively being
developed. Short, isolated proton tracks, the signal for low- momentum-transfer
neutral current (NC) elastic events, are easily hidden in a large cosmic
background. Detecting these low-energy tracks will allow us to probe
interesting regions of the proton's spin structure. An effective method for
selecting NC elastic events is to combine a highly efficient track
reconstruction algorithm to find all candidate tracks with highly accurate
particle identification using a machine learning algorithm. We present our work
on particle track classification using gradient tree boosting software
(XGBoost) and the performance on simulated neutrino data.
