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Generative Models Uncertainty Estimation by Anderlini, Lucio; Chimpoesh, Constantine; Kazeev, Nikita; Shishigina, Agata is a scholarly article available to read on EtoBox.

What is Generative Models Uncertainty Estimation about?

In recent years fully-parametric fast simulation methods based on generative models have been proposed for a variety of high-energy physics detectors. By their nature, the quality of data-driven models degrades in the regions of the phase space where the data are sparse. Since machine-learning models are hard to analyse from the physical principles, the commonly used testing procedures are performed in a data-driven way and can't be reliably used in such regions. In our work we propose three methods to estimate the uncertainty of generative models inside and outside of the training phase space region, along with data-driven calibration techniques. A test of the proposed methods on the LHCb RICH fast simulation is also presented.

Author

Anderlini, Lucio; Chimpoesh, Constantine; Kazeev, Nikita; Shishigina, Agata

Published

2022

Language

EN