Tools for unbinned unfolding

Journal Article · Fri May 23 00:00:00 EDT 2025 · Journal of Instrumentation

DOI:https://doi.org/10.1088/1748-0221/20/05/p05034· OSTI ID:2573713

Milton, Ryan ^[1]; Mikuni, Vinicius ^[2]; Lee, Trevin ^[1]; Arratia, Miguel ^[1]; Wamorkar, Tanvi ^[3]; Nachman, Benjamin ^[4]

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1. Univ. of California, Riverside, CA (United States)
2. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
3. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)
4. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States); Stanford Univ., CA (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)

Machine learning has enabled differential cross section measurements that are not discretized. Going beyond the traditional histogram-based paradigm, these unbinned unfolding methods are rapidly being integrated into experimental workflows. Here, in order to enable widespread adaptation and standardization, we develop methods, benchmarks, and software for unbinned unfolding. For methodology, we demonstrate the utility of boosted decision trees for unfolding with a relatively small number of high-level features. This complements state-of-the-art deep learning models capable of unfolding the full phase space. To benchmark unbinned unfolding methods, we develop an extension of existing dataset to include acceptance effects, a necessary challenge for real measurements. Additionally, we directly compare binned and unbinned methods using discretized inputs for the latter in order to control for the binning itself. Lastly, we have assembled two software packages for the OmniFold unbinned unfolding method that should serve as the starting point for any future analyses using this technique. One package is based on the widely-used RooUnfold framework and the other is a standalone package available through the Python Package Index (PyPI).

• Research Organization: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• Grant/Contract Number: AC02-76SF00515
• OSTI ID: 2573713
• Journal Information: Journal of Instrumentation, Journal Name: Journal of Instrumentation Journal Issue: 05 Vol. 20; ISSN 1748-0221
• Publisher: IOP Publishing
• Country of Publication: United States
• Language: English

Related Subjects

Analysis and statistical methods
Data processing methods