The landscape of unfolding with machine learning
Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov, Sascha Diefenbacher, Vinicius Mikuni, Theo Heimel, Michael Fenton, Kevin Greif, Benjamin Nachman, Daniel Whiteson, Anja Butter, Tilman Plehn
SciPost Phys. 18, 070 (2025) · published 25 February 2025
- doi: 10.21468/SciPostPhys.18.2.070
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Abstract
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.
Authors / Affiliations: mappings to Contributors and Organizations
See all Organizations.- 1 Nathan Huetsch,
- 1 Javier Mariño Villadamigo,
- 2 Alexander Shmakov,
- 3 Sascha Diefenbacher,
- 3 Vinicius Mikuni,
- 1 Theo Heimel,
- 2 Michael James Fenton,
- 2 Kevin Thomas Greif,
- 3 4 Benjamin Nachman,
- 2 Daniel Whiteson,
- 1 5 6 7 8 Anja Butter,
- 1 Tilman Plehn
- 1 Ruprecht-Karls-Universität Heidelberg / Heidelberg University
- 2 University of California, Irvine [UCI]
- 3 Lawrence Berkeley National Laboratory [LBNL]
- 4 University of California, Berkeley [UCBL]
- 5 Sorbonne Université / Sorbonne University
- 6 Centre National de la Recherche Scientifique / French National Centre for Scientific Research [CNRS]
- 7 Université de Paris / University of Paris
- 8 Laboratoire de Physique Nucléaire et de Hautes Énergies / Laboratoire de Physique Nucléaire et de Hautes Énergies [LPNHE]
- Baden-Württemberg Stiftung
- Bundesministerium für Bildung und Forschung / Federal Ministry of Education and Research [BMBF]
- Deutsche Forschungsgemeinschaft / German Research FoundationDeutsche Forschungsgemeinschaft [DFG]
- National Energy Research Scientific Computing Center
- United States Department of Energy [DOE]