240 citations · 1.9k across the 47 of their papers we have counts for
7 papers · 1 filter
How to GAN Higher Jet Resolution
Pierre Baldi, Lukas Blecher, Anja Butter +6
QCD-jets at the LHC are described by simple physics principles. We show how super-resolution generative networks can learn the underlying structures and use them to improve the res…
Simple and statistically sound recommendations for analysing physical theories
Shehu S. AbdusSalam, Fruzsina J. Agocs, Benjamin C. Allanach +72
Physical theories that depend on many parameters or are tested against data from many different experiments pose unique challenges to statistical inference. Many models in particle…
Measuring QCD Splittings with Invertible Networks
Sebastian Bieringer, Anja Butter, Theo Heimel +4
QCD splittings are among the most fundamental theory concepts at the LHC. We show how they can be studied systematically with the help of invertible neural networks. These networks…
How to GAN Event Unweighting
Mathias Backes, Anja Butter, Tilman Plehn +1
Event generation with neural networks has seen significant progress recently. The big open question is still how such new methods will accelerate LHC simulations to the level requi…
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…
GANplifying Event Samples
Anja Butter, Sascha Diefenbacher, Gregor Kasieczka +2
A critical question concerning generative networks applied to event generation in particle physics is if the generated events add statistical precision beyond the training sample.…
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…