Dimuon production in the MicroBooNE detector

Abstract

This analysis studies the production of the rare signal with two muons (dimuon) in the final state at µB using the Booster Neutrino Beam (BNB) as source of neutrino interactions. The main background source for the dimuon signal are pions. Differentiating muons and pions has been challenging at µB because the tracks left by these particles are almost identical. This analysis faces this problem using a supervised classifier algorithm called boosted decision tree (BDT). A Monte Carlo simulation (MC) enhanced dimuon signal sample was produced to train a BDT capable of recognizing this signal. A set of variables that show some discrimination characteristics was used to perform the training. A MC neutrino interaction simulation sample was used to estimate the background. Estimations of dimuon production in real data samples were made for different cases. Additionally, an analysis of the systematic uncertainties, fake data studies, and BDT performance checks were obtained.