240 citations · 1.9k across the 47 of their papers we have counts for
10 papers · 1 filter
Performance versus Resilience in Modern Quark-Gluon Tagging
Anja Butter, Barry M. Dillon, Tilman Plehn +1
Discriminating quark-like from gluon-like jets is, in many ways, a key challenge for many LHC analyses. First, we use a known difference in Pythia and Herwig simulations to show ho…
An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training
Mathias Backes, Anja Butter, Monica Dunford +1
The unfolding of detector effects is crucial for the comparison of data to theory predictions. While traditional methods are limited to representing the data in a low number of dim…
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…
Two Invertible Networks for the Matrix Element Method
Anja Butter, Theo Heimel, Till Martini +2
The matrix element method is widely considered the ultimate LHC inference tool for small event numbers. We show how a combination of two conditional generative neural networks enco…
Loop Amplitudes from Precision Networks
Simon Badger, Anja Butter, Michel Luchmann +2
Evaluating loop amplitudes is a time-consuming part of LHC event generation. For di-photon production with jets we show that simple, Bayesian networks can learn such amplitudes and…
Event Generators for High-Energy Physics Experiments
J. M. Campbell, M. Diefenthaler, T. J. Hobbs +209
We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of acti…
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…
Calomplification -- The Power of Generative Calorimeter Models
Sebastian Bieringer, Anja Butter, Sascha Diefenbacher +7
Motivated by the high computational costs of classical simulations, machine-learned generative models can be extremely useful in particle physics and elsewhere. They become especia…