Speaker
Jack Cleeve
Description
We introduce an end-to-end, self-supervised neural workflow that learns the three-dimensional electric-field distortion in a Liquid Argon TPC directly from through-going cosmic muon tracks. The network uses geometric straightness and boundary conditions to generate its own target undistorted paths and iteratively refine them. In this talk, I will present the architecture of the model, detail the self-supervised training process and analyze its performance on SBND-scale simulations.
