Evaluating Deep Learning Uncertainty Quantification Methods for Neutrino Physics Applications

We evaluate uncertainty quantification (UQ) methods for deep learning applied to liquid argon time projection chamber (LArTPC) physics analysis tasks. As deep learning applications enter widespread usage among physics data analysis, neural networks with reliable estimates of prediction uncertainty and robust performance against overconfidence and out-of-distribution (OOD) samples are critical for its full deployment in analyzing experimental data. While numerous UQ methods have been tested on simple datasets, performance evaluations for more complex tasks and datasets have been scarce. We assess the application of selected deep learning UQ methods on the task of particle classification in a simulated 3D LArTPC point cloud dataset. We observe that uncertainty enabled networks not only allow for better rejection of prediction mistakes and OOD detection, but also generally achieve higher overall accuracy across different task settings.