240 citations · 1.9k across the 47 of their papers we have counts for
4 papers · 1 filter
How to GAN Event Subtraction
Anja Butter, Tilman Plehn, Ramon Winterhalder
Subtracting event samples is a common task in LHC simulation and analysis, and standard solutions tend to be inefficient. We employ generative adversarial networks to produce new e…
How to GAN away Detector Effects
Marco Bellagente, Anja Butter, Gregor Kasieczka +2
LHC analyses directly comparing data and simulated events bear the danger of using first-principle predictions only as a black-box part of event simulation. We show how simulations…
How to GAN LHC Events
Anja Butter, Tilman Plehn, Ramon Winterhalder
Event generation for the LHC can be supplemented by generative adversarial networks, which generate physical events and avoid highly inefficient event unweighting. For top pair pro…
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…