https://doi.org/10.1140/epjc/s10052-025-15265-9
Regular Article - Computing, Software and Data Science
A practical guide to unbinned unfolding
1
Department of Physics, University of Zurich, 8006, Zurich, Switzerland
2
Department of Physics, University of Colorado Boulder, 80309, Boulder, CO, USA
3
Department of Physics, Carleton University, K1S 5B6, Ottawa, ON, Canada
4
Physics Division, Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA
5
Department of Physics and Astronomy, University of Tennessee at Knoxville, 37996, Knoxville, TN, USA
6
Kobayashi-Maskawa Institute, Nagoya University, Nagoya, Japan
7
TRIUMF, V6T 2A3, Vancouver, BC, Canada
8
Department of Particle Physics and Astrophysics, Stanford University, 94305, Stanford, CA, USA
9
Fundamental Physics Directorate, SLAC National Accelerator Laboratory, 94025, Menlo Park, CA, USA
10
Wright Laboratory, Yale University, 06511, New Haven, CT, USA
11
Department of Physics and Astronomy, Rutgers University, 08901, New Brunswick, NJ, USA
12
Department of Physics, University of Wisconsin-Madison, 53706, Madison, WI, USA
13
Google, New York, USA
a
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Received:
4
October
2025
Accepted:
23
December
2025
Published online:
2
February
2026
Unfolding, in the context of high-energy particle physics, refers to the process of removing detector distortions in experimental data. The resulting unfolded measurements are straightforward to use for direct comparisons between experiments and a wide variety of theoretical predictions. For decades, popular unfolding strategies were designed to operate on data formatted as one or more binned histograms. In recent years, new strategies have emerged that use machine learning to unfold datasets in an unbinned manner, allowing for higher-dimensional analyses and more flexibility for current and future users of the unfolded data. This guide comprises recommendations and practical considerations from researchers across a number of major particle physics experiments who have recently put these techniques into practice on real data.
Florencia Canelli, Kyle Cormier, Weijie Jin: CMS Collaboration.
Andrew Cudd, Roger G. Huang: T2K Collaboration.
Dag Gillberg, Benjamin Nachman, Jingjing Pan, Mariel Pettee: ATLAS Collaboration.
Vinicius Mikuni, Benjamin Nachman, Jingjing Pan, Fernando Torales Acosta: H1 Collaboration.
Sookhyun Lee: LHCb Collaboration.
Tanmay Pani, Youqi Song: STAR Collaboration.
© The Author(s) 2026
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Funded by SCOAP3.
