7 papers
Generative Networks for LHC events
Anja Butter, Tilman Plehn
LHC physics crucially relies on our ability to simulate events efficiently from first principles. Modern machine learning, specifically generative networks, will help us tackle sim…
Invertible Networks or Partons to Detector and Back Again
Marco Bellagente, Anja Butter, Gregor Kasieczka +5
For simulations where the forward and the inverse directions have a physics meaning, invertible neural networks are especially useful. A conditional INN can invert a detector simul…
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 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…
The Gauge-Higgs Legacy of the LHC Run II
Anke Biekötter, Tyler Corbett, Tilman Plehn
We present a global analysis of the Higgs and electroweak sector based on LHC Run II and electroweak precision observables. We show which measurements provide the leading constrain…