Speaker
Sofia Palacios Schweitzer
(ITP, University Heidelberg)
Description
Two shortcomings of classical unfolding algorithms, namely that they are defined on binned, one-dimensional observables, can be overcome when using generative machine learning. Many studies on generative unfolding reduce the problem to correcting for detector smearing, however a full unfolding pipeline must also account for background, acceptance and efficiency effects. To fully integrate generative unfolding into existing analysis pipelines at the LHC, we develop solutions for these crucial but often overlooked aspects.
Authors
Anja Butter
(Centre National de la Recherche Scientifique (FR))
Ben Nachman
(Lawrence Berkeley National Lab. (US))
Nathan Huetsch
(Heidelberg University, ITP Heidelberg)
Sascha Diefenbacher
(Lawrence Berkeley National Lab. (US))
Sofia Palacios Schweitzer
(ITP, University Heidelberg)
Vinicius Massami Mikuni
(Lawrence Berkeley National Lab. (US))
