2024/11/15 by D. Douglas, Douglas, Daniel, Daniel Ratner +5 · 1 citation
Computer Science · Engineering · Physics and Astronomy · #Advanced Data Processing Techniques #Computational Physics (physics.comp-ph) #Computational Physics and Python Applications #Data Analysis #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Scientific Research and Discoveries #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2411.09864
openalex publication_date 2024/11/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Sequential or chained models are increasingly prevalent in machine learning for scientific applications, due to their flexibility and ease of development. Chained models are particularly useful when a task is separable into distinct steps with a hierarchy of meaningful intermediate representations. In reliability-critical tasks, it is important to quantify the confidence of model inferences. However, chained models pose an additional challenge for uncertainty quantification, especially when input uncertainties need to be propagated. In such cases, a fully uncertainty-aware chain of models is required, where each step accepts a probability distribution over the input space, and produces a probability distribution over the output space. In this work, we present a case study for adapting a single model within an existing chain, designed for reconstruction within neutrino-Argon interactions, developed for neutrino oscillation experiments such as MicroBooNE, ICARUS, and the future DUNE experiment. We test the performance of an input uncertainty-enabled model against an uncertainty-blinded model using a method for generating synthetic noise. By comparing these two, we assess the increase in inference quality achieved by exposing models to upstream uncertainty estimates.