Speaker
Sebastian Guido Bieringer
(Hamburg University)
Description
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 [1] and large ranges of training sample sizes. In this talk, we extend this histogram based method to show the same effect for a physics simulation, specifically photon showers in an electromagnetic calorimeter [2].
[1] https://arxiv.org/abs/2008.06545
[2] https://arxiv.org/abs/2202.07352
Author
Sebastian Guido Bieringer
(Hamburg University)
Co-authors
Anja Butter
Ben Nachman
(Lawrence Berkeley National Lab. (US))
Daniel Hundhausen
(Deutsches Elektronen-Synchrotron DESY)
Engin Eren
Frank-Dieter Gaede
(Deutsches Elektronen-Synchrotron (DE))
Gregor Kasieczka
(Hamburg University (DE))
Prof.
Mathias Trabs
( Department of Mathematics, Karlsruhe Institute of Technology, Germany)
Sascha Daniel Diefenbacher
(Hamburg University (DE))
Tilman Plehn
