Speaker
Description
Numerous new physics models predict massive long-lived particles that can decay to muon pairs. However, searching for such dimuon beyond standard model signals in liquid argon time projection chamber-based neutrino experiments is challenging because of the nearly irreducible neutrino background that includes one muon and one pion in the final states. The primary limiting factor is the insufficient distinction between muons and pions due to their similar mass and minimum ionization energy deposition profile. To address this issue, we developed a novel AI-ML method using the Optimal Transport (OT) algorithm, which showed promise in classifying jets in large hadron collider data. Using a set of publicly available MicroBooNE simulation data containing about 10K well-reconstructed fully contained muon and pion tracks, we adapted this OT tool to separate muons from pions. Our OT algorithm leverages full 3D imaging of space points along the track trajectory with a focus on the difference in decay and capture behavior of the two particles in argon. The algorithm is optimized to correctly identify muons, ensuring high confidence in dimuon searches when requiring both tracks of two-track events to be classified as muons. The efficiency improvements for muon selection at 90% accuracy using the new OT method will be shown.
