July 31, 2017 to August 4, 2017
Fermi National Accelerator Laboratory
US/Central timezone

Automated proton track identification in MicroBooNE using gradient boosted decision trees

Aug 3, 2017, 11:51 AM
Hornets Nest (Fermi National Accelerator Laboratory)

Hornets Nest

Fermi National Accelerator Laboratory

Presentation Computing, Analysis Tools and Data Handling Computing, Analysis Tools, and Data Handling


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