Speaker
Antoine Petitjean
(ITP, Universität Heidelberg)
Description
Unfolding detector-level data into meaningful particle-level distributions remains a key challenge in collider physics, especially as the dimensionality of the relevant observables increases. Traditional unfolding techniques often struggle with such high-dimensional problems, motivating the development of machine learning-based approaches.We introduce a new method for generative unfolding that is designed to handle many variables simultaneously, incorporating state-of-the-art model design choices.
Significance
We present a new method for generative unfolding in a large number of dimensions
Authors
Antoine Petitjean
(ITP, Universität Heidelberg)
Tilman Plehn