Speaker
Sofia Palacios Schweitzer
(ITP, Heidelberg University)
Description
Many physics analyses at the LHC rely on algorithms to remove detector effect, commonly known as unfolding. Whereas classical methods only work with binned, one-dimensional data, Machine Learning promises to overcome both problems. Using a generative unfolding pipeline, we show how it can be build into an existing LHC analysis, designed to measure the top mass. We discuss the model-dependence of our algorithm, i.e. the bias of our measurement towards the top mass used in simulation and propose a method to reliably achieve unbiased results.
Authors
Alexander Paasch
(Hamburg University)
Dennis Schwarz
(Austrian Academy of Sciences)
Luigi Favaro
Roman Kogler
(University of Hamburg)
Sofia Palacios Schweitzer
(ITP, Heidelberg University)
Tilman Plehn
(Heidelberg University)