Generative models uncertainty estimation

Understand

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.