Recent results on a machine learning approach to event position reconstruction in the DEAP-3600 Dark Matter Search Experiment

Machine learning is increasingly being applied in elementary particle physics, and the DEAP-3600 dark matter detector is no exception. One application of the new algorithm is the event position reconstruction in the detector. Here, we present updated results on the application of a fully-connected neural network for quality improvement. We also describe the structure of the neural network, its changes from the previous version, and a comparison with existing event position reconstruction algorithms.