Calorimetric classification of track-like signatures in liquid argon TPCs using MicroBooNE data

TL;DR

This paper introduces a new particle identification method for liquid argon TPCs that compensates for non-uniform calorimetric reconstruction, improving accuracy in identifying protons and muons in MicroBooNE data.

Contribution

The work presents a novel particle identification algorithm that addresses non-uniform calorimetric performance, enhancing identification efficiency and reducing misclassification in liquid argon TPCs.

Findings

Achieved 94% proton selection efficiency

Reduced muon mis-identification rate to 10%

Demonstrated applicability to future LArTPC experiments

Abstract

The MicroBooNE liquid argon time projection chamber located at Fermilab is a neutrino experiment dedicated to the study of short-baseline oscillations, the measurements of neutrino cross sections in liquid argon, and to the research and development of this novel detector technology. Accurate and precise measurements of calorimetry are essential to the event reconstruction and are achieved by leveraging the TPC to measure deposited energy per unit length along the particle trajectory, with mm resolution. We describe the non-uniform calorimetric reconstruction performance in the detector, showing dependence on the angle of the particle trajectory. Such non-uniform reconstruction directly affects the performance of the particle identification algorithms which infer particle type from calorimetric measurements. This work presents a new particle identification method which accounts for and effectively addresses such non-uniformity. The newly developed method shows improved performance compared to previous algorithms, illustrated by a 94% proton selection efficiency and a 10% muon mis-identification rate, with a fairly loose selection of tracks performed on beam data. The performance is further demonstrated by identifying exclusive final states in $\nu_{\mu} CC$ interactions. While developed using MicroBooNE data and simulation, this method is easily applicable to future LArTPC experiments, such as SBND, ICARUS, and DUNE.