Calomplification: the power of generative calorimeter models - Bieringer, Sebastian; Butter, Anja; Diefenbacher, Sascha; Eren, Engin; Gaede, Frank; Hundhausen, Daniel; Kasieczka, Gregor; Nachman, Benjamin; Plehn, Tilman; Trabs, Mathias (Arxiv, 2022)
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.
Gesehen am 15.09.2022