Speaker
Nathan Huetsch
(Heidelberg University, ITP Heidelberg)
Description
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new methods for ML-based unfolding. The performance of these approaches is evaluated on two benchmark datasets. We find that all techniques are capable of accurately reproducing the particle-level spectra across complex observables. Given that these approaches are conceptually diverse, they offer an exciting toolkit for a new class of measurements that can probe the Standard Model with an unprecedented level of detail and may enable sensitivity to new phenomena.
Track | Unfolding |
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Authors
Alexander Shmakov
(University of California Irvine (US))
Anja Butter
(Centre National de la Recherche Scientifique (FR))
Ben Nachman
(Lawrence Berkeley National Lab. (US))
Daniel Whiteson
(University of California Irvine (US))
Javier Mariño Villadamigo
(Institut für Theoretische Physik - University of Heidelberg)
Kevin Thomas Greif
(University of California Irvine (US))
Michael James Fenton
(University of California Irvine (US))
Nathan Huetsch
(Heidelberg University, ITP Heidelberg)
Sascha Diefenbacher
(Lawrence Berkeley National Lab. (US))
Theo Heimel
(Heidelberg University)
Tilman Plehn
(Heidelberg University)
Vinicius Massami Mikuni
(Lawrence Berkeley National Lab. (US))