Aug 21 – 25, 2017
University of Washington, Seattle
US/Pacific timezone

Automated proton track identification in MicroBooNE using gradient boosted decision trees

Aug 24, 2017, 3:40 PM
107 (Alder Hall)


Alder Hall

Oral Track 2: Data Analysis - Algorithms and Tools Track 2: Data Analysis - Algorithms and Tools


Katherine Woodruff (New Mexico State University)


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.

Primary author

Katherine Woodruff (New Mexico State University)

Presentation materials

Peer reviewing