Speaker
Felix Yu
(Harvard University)
Description
Convolutional neural networks (CNNs) have seen extensive applications in scientific data analysis, including in neutrino telescopes. However, the data from these experiments present numerous challenges to CNNs, such as non-regular geometry, sparsity, and high dimensionality. In this talk, I will present sparse submanifold convolutions (SSCNNs) as a solution to these issues and show that the SSCNN event reconstruction performance is comparable to or better than traditional and machine learning algorithms. I will also discuss our current efforts to implement this type of network into the IceCube Neutrino Observatory.
Primary author
Felix Yu
(Harvard University)
