1 citations · 1 across the 1 of their papers we have counts for
3 papers
Event-by-event Comparison between Machine-Learning- and Transfer-Matrix-based Unfolding Methods
Mathias Backes, Anja Butter, Monica Dunford +1
The unfolding of detector effects is a key aspect of comparing experimental data with theoretical predictions. In recent years, different Machine-Learning methods have been develop…
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…
How to GAN Event Unweighting
Mathias Backes, Anja Butter, Tilman Plehn +1
Event generation with neural networks has seen significant progress recently. The big open question is still how such new methods will accelerate LHC simulations to the level requi…