- 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.