4 papers
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…
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…
CapsNets Continuing the Convolutional Quest
Sascha Diefenbacher, Hermann Frost, Gregor Kasieczka +2
Capsule networks are ideal tools to combine event-level and subjet information at the LHC. After benchmarking our capsule network against standard convolutional networks, we show h…
Dark Matter in Anomaly-Free Gauge Extensions
Martin Bauer, Sascha Diefenbacher, Tilman Plehn +2
A consistent model for vector mediators to dark matter needs to be anomaly-free and include a scalar mode from mass generation. For the leading U(1) extensions we review the struct…