Liquid Argon Time Projection Chambers (LAr TPCs) provide detailed imaging of neutrino interactions, making them an ideal environment for machine learning based classification tasks. While conventional neural networks have achieved strong performance in this domain, they do not naturally account for uncertainties, an essential requirement for robust physics analyses. Bayesian Neural Networks...
It is well-known that classifiers can be trained to approximate the likelihood ratio between two distributions, and that machine learning based classifiers in particular can learn this likelihood ratio in high-dimensional spaces. This provides a method to reweight events from different distributions as functions of many features. OmniFold is an unfolding technique that uses this concept to...
Quantifying systematic uncertainties in particle physics analyses is complicated by the need to estimate detector responses through Monte Carlo (MC) simulations. Conventional approaches compute variations in reconstructed variables under fixed physics assumptions, which can bias the parameters being measured. We present a likelihood-free inference method that uses neural networks to derive...
