Speaker
Description
Many current forefront precision neutrino experiments are studying neutrinos and their properties, including the MicroBooNE experiment. The MicroBooNE detector employs a liquid argon time projection chamber (LArTPC), a detector technology at the forefront of the field due to its excellent capabilities in tracking, calorimetry, and particle identification. However, like most LArTPCs, the MicroBooNE detector lacks a magnetic field and therefore a straightforward method to distinguish charged particles from their antiparticles. Measurements of muon antineutrino charged-current ($\bar{\nu}_\mu \text{CC}$) interactions are important to CP-violation searches as the observation of CP violation relies on comparisons between neutrino and antineutrino oscillation probabilities. Furthermore, measuring the $\bar{\nu}_\mu \text{CC}$ cross section at MicroBooNE can aid the interaction modeling in such CP violation searches. This analysis provides a method to isolate the $\bar{\nu}_\mu \text{CC}$ and $\nu_\mu \text{CC}$ distribution despite the absence of a magnetic field by employing machine-learning techniques, specifically boosted decision trees, to separate $\nu_\mu \text{CC}$ and $\bar{\nu}_\mu \text{CC}$ interaction events using kinematic differences between them. Once the separation is achieved, this approach will enable the extraction of the individual total $\nu_\mu$ and $\bar{\nu}_\mu$ CC cross sections on argon. This will allow us to measure the $\bar{\nu}_\mu \text{CC}$ total cross section on argon in MicroBooNE. This poster presents the current status of the simulation studies performed using MicroBooNE NuMI Run 3 reversed horn current samples and outlines progress toward the extraction of the individual total cross sections.