How to understand limitations of generative networks - Das, Ranit; Favaro, Luigi; Heimel, Theo; Krause, Claudius; Plehn, Tilman; Shih, David (SciPost physics, 2024)
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.
Veröffentlicht: 25. Januar 2024
Gesehen am 05.06.2024