https://doi.org/10.1140/epjc/s10052-025-14835-1
Regular Article - Experimental Physics
Unbinned inference with correlated events
1
Physics Division, Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA
2
Department of Physics, University of California, 94720, Berkeley, CA, USA
3
Department of Physics and Astronomy, University of California, 92521, Riverside, CA, USA
4
Berkeley Institute for Data Science, University of California, 94720, Berkeley, CA, USA
a
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Received:
9
May
2025
Accepted:
19
September
2025
Published online:
1
October
2025
Abstract
Modern machine learning has enabled parameter inference from event-level data without the need to first summarize all events with a histogram. All of these unbinned inference methods make use of the fact that the events are statistically independent so that the log likelihood is a sum over events. However, this assumption is not valid for unbinned inference on unfolded data, where the deconvolution process induces a correlation between events. We explore the impact of event correlations on downstream inference tasks in the context of the OmniFold unbinned unfolding method. We find that uncertainties may be significantly underestimated when event correlations are excluded from uncertainty quantification.
© The Author(s) 2025
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