Neutrino interaction vertex-finding in a DUNE far-detector using Pandora deep-learning

The Deep Underground Neutrino Experiment (DUNE) will operate four large-scale Liquid-Argon Time-Projection Chambers (LArTPCs) at the far site in South Dakota, producing high-resolution images of neutrino interactions. Effective utilisation of the LArTPC imaging capabilities requires advanced, automated pattern-recognition techniques to reconstruct the visible particles comprising these images. A critical component is the identification of the neutrino interaction vertex, which is non-trivial due to the interaction occurring at any point within the detector volume, because subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. The new vertex-finding algorithm presented in this poster builds upon the previously reported uses of convolutional neural networks within the Pandora multi-algorithm approach to pattern recognition, integrating traditional pattern-recognition approaches with machine-learning