276 citations · 1.6k across the 18 of their papers we have counts for
19 papers
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…
Symmetries, Safety, and Self-Supervision
Barry M. Dillon, Gregor Kasieczka, Hans Olischlager +3
Collider searches face the challenge of defining a representation of high-dimensional data such that physical symmetries are manifest, the discriminating features are retained, and…
Unsupervised Hadronic SUEP at the LHC
Jared Barron, David Curtin, Gregor Kasieczka +2
Confining dark sectors with pseudo-conformal dynamics produce SUEP, or Soft Unclustered Energy Patterns, at colliders: isotropic dark hadrons with soft and democratic energies. We…
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…
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.…
Towards Machine Learning Analytics for Jet Substructure
Gregor Kasieczka, Simone Marzani, Gregory Soyez +1
The past few years have seen a rapid development of machine-learning algorithms. While surely augmenting performance, these complex tools are often treated as black-boxes and may i…
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…
Per-Object Systematics using Deep-Learned Calibration
Gregor Kasieczka, Michel Luchmann, Florian Otterpohl +1
We show how to treat systematic uncertainties using Bayesian deep networks for regression. First, we analyze how these networks separately trace statistical and systematic uncertai…
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…
CapsNets Continuing the Convolutional Quest
Sascha Diefenbacher, Hermann Frost, Gregor Kasieczka +2
Capsule networks are ideal tools to combine event-level and subjet information at the LHC. After benchmarking our capsule network against standard convolutional networks, we show h…
Deep-Learning Jets with Uncertainties and More
Sven Bollweg, Manuel Haussmann, Gregor Kasieczka +3
Bayesian neural networks allow us to keep track of uncertainties, for example in top tagging, by learning a tagger output together with an error band. We illustrate the main featur…
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…
Quark-Gluon Tagging: Machine Learning vs Detector
Gregor Kasieczka, Nicholas Kiefer, Tilman Plehn +1
Distinguishing quarks from gluons based on low-level detector output is one of the most challenging applications of multi-variate and machine learning techniques at the LHC. We fir…
QCD or What?
Theo Heimel, Gregor Kasieczka, Tilman Plehn +1
Autoencoder networks, trained only on QCD jets, can be used to search for anomalies in jet-substructure. We show how, based either on images or on 4-vectors, they identify jets fro…
Deep-learned Top Tagging with a Lorentz Layer
Anja Butter, Gregor Kasieczka, Tilman Plehn +1
We introduce a new and highly efficient tagger for hadronically decaying top quarks, based on a deep neural network working with Lorentz vectors and the Minkowski metric. With its…
Deep-learning Top Taggers or The End of QCD?
Gregor Kasieczka, Tilman Plehn, Michael Russell +1
Machine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepT…
Resonance Searches with an Updated Top Tagger
Gregor Kasieczka, Tilman Plehn, Torben Schell +2
The performance of top taggers, for example in resonance searches, can be significantly enhanced through an increased set of variables, with a special focus on final-state radiatio…
Benchmarking an Even Better HEPTopTagger
Christoph Anders, Catherine Bernaciak, Gregor Kasieczka +2
Top taggers are established analysis tools to reconstruct boosted hadronically decaying top quarks for example in searches for heavy resonances. We first present a dedicated study…