172 citations · 821 across the 34 of their papers we have counts for
4 papers · 1 filter
MadNIS -- Neural Multi-Channel Importance Sampling
Theo Heimel, Ramon Winterhalder, Anja Butter +5
Theory predictions for the LHC require precise numerical phase-space integration and generation of unweighted events. We combine machine-learned multi-channel weights with a normal…
Modern Machine Learning for LHC Physicists
Tilman Plehn, Anja Butter, Barry Dillon +3
Depending on the point of view, modern machine learning is either providing an unprecedented boost to the numerical methods of particle physics, or it is transforming the way we do…
Machine Learning and LHC Event Generation
Anja Butter, Tilman Plehn, Steffen Schumann +48
First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predicti…
Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows
Anja Butter, Sascha Diefenbacher, Gregor Kasieczka +4
The large data rates at the LHC require an online trigger system to select relevant collisions. Rather than compressing individual events, we propose to compress an entire data set…