17 citations · 18 across the 2 of their papers we have counts for
7 papers
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…
Deep-Learning Jets with Uncertainties and More
Sven Bollweg, Manuel Haussmann, Gregor Kasieczka +3
Bayesian neural networks allow us to keep track of uncertainties, for example in top tagging, by learning a tagger output together with an error band. We illustrate the main featur…
The Machine Learning Landscape of Top Taggers
G. Kasieczka, T. Plehn, A. Butter +24
Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established metho…
Spotting hidden sectors with Higgs binoculars
Monika Blanke, Simon Kast, Jennifer M. Thompson +2
We explore signals of new physics with two Higgs bosons and large missing transverse energy at the LHC. Such a signature is characteristic of models for dark matter or other seclud…
Quark-Gluon Tagging: Machine Learning vs Detector
Gregor Kasieczka, Nicholas Kiefer, Tilman Plehn +1
Distinguishing quarks from gluons based on low-level detector output is one of the most challenging applications of multi-variate and machine learning techniques at the LHC. We fir…
QCD or What?
Theo Heimel, Gregor Kasieczka, Tilman Plehn +1
Autoencoder networks, trained only on QCD jets, can be used to search for anomalies in jet-substructure. We show how, based either on images or on 4-vectors, they identify jets fro…