23 citations · 42 across the 7 of their papers we have counts for
4 papers · 1 filter
Anomaly detection with flow-based fast calorimeter simulators
Claudius Krause, Benjamin Nachman, Ian Pang +2
Recently, several normalizing flow-based deep generative models have been proposed to accelerate the simulation of calorimeter showers. Using CaloFlow as an example, we show that t…
Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
Baran Hashemi, Claudius Krause
In modern collider experiments, the quest to explore fundamental interactions between elementary particles has reached unparalleled levels of precision. Signatures from particle ph…
Normalizing Flows for High-Dimensional Detector Simulations
Florian Ernst, Luigi Favaro, Claudius Krause +2
Whenever invertible generative networks are needed for LHC physics, normalizing flows show excellent performance. In this work, we investigate their performance for fast calorimete…
Combining Resonant and Tail-based Anomaly Detection
Gerrit Bickendorf, Manuel Drees, Gregor Kasieczka +2
In many well-motivated models of the electroweak scale, cascade decays of new particles can result in highly boosted hadronic resonances (e.g. ). This can make these models…