Training neural networks to operate on three-dimensional trajectories from particle detectors is challenging due to the large combinatorial complexity of the data in three dimensions. Using networks that incorporate Euclidian Equivariance could prove to be very beneficial in reducing the need for data augmentation. Our focus is on data from neutrino experiments using liquid argon time...
MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the measured MiniBooNE low energy excess. The LArTPC technology pioneered by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer...
We present NuGraph2, a Graph Neural Network (GNN) for reconstruction of liquid argon time projection chamber (LArTPC) data, developed as part of the ExaTrkX project. We discuss the network architecture, a multi-head attention message passing network that classifies detector hits according to the particle type that produced them. By utilizing a heterogeneous graph structure with independent...
