Convolutional Visual Networks for Secondary Vertexing in NOvA*

NOvA is a Fermilab-based long-baseline neutrino experiment designed primarily to measure electron neutrino appearance and muon neutrino disappearance in a predominantly muon neutrino beam. NOvA also has a broad physics program including neutrino cross-section measurements in the near detector and searches for beyond-standard model phenomena in both detectors. The ability to accurately identify and reconstruct the energies of individual particles within an event is crucial for cross section measurements. NOvA's current reconstruction techniques lack the ability to identify in-detector reinteractions or decays, which can lead to incorrect assignment of energy within an event or misidentification of tracks within the detector. In this contribution, we describe progress toward training a convolutional neural network to provide a "secondary vertexing" capability. We present this methodology in detail and report on the impact this capability will have on track/event reconstruction in NOvA.