Speaker
Alejandro Yankelevich
(University of California, Irvine)
Description
The NOvA experiment uses a convolutional neural network (CNN) that analyzes topological features to determine neutrino flavor. Alternative approaches to flavor identification using machine learning are being investigated with the goal of developing a network trained with both event-level and particle-level images in addition to reconstructed physical variables while maintaining the performance of the CNN. Such a network could be used to analyze the individual prediction importances of these inputs. An original network that uses a combination of transformer and MobileNet CNN blocks will be discussed.
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Authors
Alejandro Yankelevich
(University of California, Irvine)
Alexander Shmakov
(University of California, Irvine)
