Speaker
Javier Mariño Villadamigo
(Institut für Theoretische Physik - University of 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 are evaluated on the same two 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.
| Primary Field of Research | Particle Physics |
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Primary authors
Nathan Huetsch
(Heidelberg University, ITP Heidelberg)
Javier Mariño Villadamigo
(Institut für Theoretische Physik - University of Heidelberg)
Alexander Shmakov
(University of California Irvine (US))
Co-authors
Sascha Diefenbacher
(Lawrence Berkeley National Lab. (US))
Vinicius Massami Mikuni
(Lawrence Berkeley National Lab. (US))
Theo Heimel
(Heidelberg University)
Michael James Fenton
(University of California Irvine (US))
Kevin Thomas Greif
(University of California Irvine (US))
Ben Nachman
(Lawrence Berkeley National Lab. (US))
Daniel Whiteson
(University of California Irvine (US))
Anja Butter
(Centre National de la Recherche Scientifique (FR))
Tilman Plehn
