Speaker
Omar Alterkait
(Tufts University / IAIFI)
Description
Training neural networks for analyzing three-dimensional trajectories in particle detectors presents challenges due to the high combinatorial complexity of the data. Incorporating networks with Euclidean Equivariance could significantly reduce the reliance on data augmentation. To achieve Euclidean Equivariance, we construct neural networks that primarily represent data and perform convolutions as functions of spherical harmonics. Our primary focus is on data from neutrino experiments utilizing liquid argon time projection chambers.
| Type of contribution | Talk: 30 minutes. |
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Primary author
Omar Alterkait
(Tufts University / IAIFI)
