Generative models uncertainty estimation
This addresses uncertainty estimation for generative models in high-energy physics, particularly in data-sparse regions, but is incremental as it builds on existing simulation methods.
The authors tackled the problem of generative model uncertainty estimation in high-energy physics detectors, proposing three methods with data-driven calibration and demonstrating them on LHCb RICH fast simulation.
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.