Searching for Short-Range Correlations in $^{40}AR$
S. Sword-Fehlberg · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2022
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. Short range correlations (SRCs) are an example of such an effect that are known to contribute non-trivially to the CC cross section. In MicroBooNE, a CC interaction with a correlated nucleon would lead to a 1 muon, 2 proton topology (CC2p). In order to study SRCs in MicroBooNE, It is crucial that the particle identification (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 towards the development of a CNN for CC2p event selection and identification.