Automated proton track identification in MicroBooNE using gradient boosted decision trees

Katherine Woodruff

doi:10.1088/1742-6596/1085/4/042019

Abstract

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.

Highlights

The three up and down valence quarks in the nucleon only account for a small percent of its mass
The decision tree model We created a multi-class gradient-boosted decision tree classifier, using the XGBoost software library, to separate five different track types: any proton track, muons or pions from BNB neutrino interactions, tracks from electromagnetic showers from BNB interactions, and any non-proton track produced by a cosmic ray interaction
For neutral current (NC) elastic events, we would use the output of the decision trees along with other event information such as the total number of reconstructed tracks to select the events of interest

Summary

Introduction

The three up and down valence quarks in the nucleon only account for a small percent of its mass. The neutral-current form factors, GNA C , GNE C , and GNMC , are functions of Q2 and can all be written as a linear combination of the individual quark contributions MicroBooNE can detect protons with a track length of at least 1.5 cm which corresponds to a kinetic energy of ∼40 MeV in liquid argon giving Q2 ∼ 0.08 GeV2.

Results

Conclusion