Calomplification — the power of generative calorimeter models. (22nd September 2022) - Bieringer, S.; Butter, A.; Diefenbacher, S.; Eren, E.; Gaede, F.; Hundhausen, D.; Kasieczka, G.; Nachman, B.; Plehn, T.; Trabs, M.
Abstract: Motivated by the high computational costs of classical simulations, machine-learned generative models can be extremely useful in particle physics and elsewhere. They become especially attractive when surrogate models can efficiently learn the underlying distribution, such that a generated sample outperforms a training sample of limited size. This kind of GANplification has been observed for simple Gaussian models. We show the same effect for a physics simulation, specifically photon showers in an electromagnetic calorimeter.