Speaker
Sofia Palacios Schweitzer
(Universität Heidelberg)
Description
When doing analyses in particle physics we are often faced with the task of correcting our reconstructed observables for detector effects, commonly known as unfolding. While traditional unfolding methods are restricted to binned distributions of a single observable, ML-based methods enable unbinned, high-dimensional unfolding over the entire phase space. In this talk I will introduce generative unfolding where a conditional neural network is used to learn the unfolded distribution conditioned on the reconstructed one.
Primary author
Sofia Palacios Schweitzer
(Universität Heidelberg)
