Automated proton track identification in MicroBooNE using gradient boosted decision trees

TL;DR

This paper presents a machine learning approach using gradient boosted decision trees to accurately identify proton tracks in MicroBooNE's liquid argon detector, improving detection of low-energy neutrino interactions.

Contribution

The study introduces a novel application of XGBoost for particle track classification in LArTPC data, enhancing low-energy proton detection accuracy.

Findings

High classification accuracy achieved on simulated data

Effective separation of proton tracks from background

Potential to improve neutrino interaction analysis

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.