2 papers
physics.ins-det2021
Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network
Erik Buhmann, Sascha Diefenbacher, Engin Eren +4
Given the increasing data collection capabilities and limited computing resources of future collider experiments, interest in using generative neural networks for the fast simulati…
hep-ph2020
DCTRGAN: Improving the Precision of Generative Models with Reweighting
Sascha Diefenbacher, Engin Eren, Gregor Kasieczka +3
Significant advances in deep learning have led to more widely used and precise neural network-based generative models such as Generative Adversarial Networks (GANs). We introduce a…