The landscape of unfolding with machine learning
SciPost Journals Publication Detail SciPost Phys. 18, 070 (2025) The landscape of unfolding with machine learning
Huetsch, Nathan↗
Engineering topics
Heimel, Theo
Publications and source records attributed to Heimel, Theo.
How to understand limitations of generative networks
Well-trained classifiers and their complete weight distributions provide us with a well-motivated and practicable method to test generative networks in particle physics. We illustrate their benefits for distribution-shifted jets, calorimeter showers, and reconstruction-level events. In all cases, the classifier weights make for a powerful test of the generative network, identify potential problems in the density estimation, relate them to the underlying physics, and tie in with a comprehensive precision and uncertainty treatment for generative networks.
Physics↗
MadNIS - Neural multi-channel importance sampling
Theory predictions for the LHC require precise numerical phase-space integration and generation of unweighted events. We combine machine-learned multi-channel weights with a normalizing flow for importance sampling, to improve classical methods for numerical integration. We develop an efficient bi-directional setup based on an invertible network, combining online and buffered training for potentially expensive integrands. We illustrate our method for the Drell-Yan process with an additional narrow resonance.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗