Calibrating Bayesian Generative Machine Learning for Bayesiamplification

The upcoming high-luminosity runs of the LHC will push the quantitative frontier of data taking to over 25-times its current rates. To ensure precision gains from such high statistics, this increase in experimental data needs to be met by an equal amount of simulation. The required computational power is predicted to outgrow the increase in budget in the coming years [1, 2]. One solution to this predicament is the augmentation of the expensive, Monte Carlo-based, simulation chain with generative machine learning. A special focus is often put on the costly detector simulation [3, 4]. This approach is only viable under the assumption that the generated data is not statistically limited to the size of the simulated training data. Previous studies have shown, for both toy data [5] and calorimeter images [6], that samples generated with generative neural networks can surpass the training statistics due to powerful interpolation abilities of the network in data space. These studies rely on comparing a distance measure between histograms of generated data and true hold-out data to the distance between smaller, statistically limited sets of Monte Carlo data and the hold-out set. The phenomenon of a generative model surpassing the precision of its training set is also known as amplification.