Studying Nuclear Effects in the MicroBooNE Detector

The neutrino charged current (CC) cross section is critical for understanding neutrino oscillations as the flavor of a neutrino can only be determined by its CC interaction. As future generations of neutrino detectors will use argon as a target nucleus, it’s important to understand the influence that nuclear effects have on neutrino-nucleon cross sections. The MicroBooNE collaboration has developed a CC inclusive selection which uses a number of advanced reconstruction algorithms and cosmic rejection tools. The same selection can be used with particle identification (PID) techniques to study nuclear effects associated with exclusive CC channels, such as short-range nucleon-nucleon correlations. In MicroBooNE, a CC interaction with a correlated nucleon would lead to a 1 muon, 2 proton topology (CC2p). It is crucial that the PID techniques used are efficient in identifying CC2p signals from background. Convolutional neural networks (CNN) have shown great promise in their ability to distinguish objects in an image from a noisy background. We present here our work in further constraining the CC inclusive selection and the development of a CNN for CC2p event identification.