8 citations · 8 across the 1 of their papers we have counts for
6 papers
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka +66
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of i…
Pay Attention To Mean Fields For Point Cloud Generation
Benno Käch, Isabell Melzer-Pellmann, Dirk Krücker
Collider data generation with machine learning has become increasingly popular in particle physics due to the high computational cost of conventional Monte Carlo simulations, parti…
DeepTreeGAN: Fast Generation of High Dimensional Point Clouds
Moritz Alfons Wilhelm Scham, Dirk Krücker, Benno Käch +1
In High Energy Physics, detailed and time-consuming simulations are used for particle interactions with detectors. To bypass these simulations with a generative model, the generati…
Attention to Mean-Fields for Particle Cloud Generation
Benno Käch, Isabell Melzer-Pellmann
The generation of collider data using machine learning has emerged as a prominent research topic in particle physics due to the increasing computational challenges associated with…
JetFlow: Generating Jets with Conditioned and Mass Constrained Normalising Flows
Benno Käch, Dirk Krücker, Isabell Melzer-Pellmann +3
Fast data generation based on Machine Learning has become a major research topic in particle physics. This is mainly because the Monte Carlo simulation approach is computationally…
Point Cloud Generation using Transformer Encoders and Normalising Flows
Benno Käch, Dirk Krücker, Isabell Melzer-Pellmann
Data generation based on Machine Learning has become a major research topic in particle physics. This is due to the current Monte Carlo simulation approach being computationally ch…