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Handling uncertainties in background shapes: the discrete profiling method

5 Pith papers cite this work. Polarity classification is still indexing.

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abstract

A common problem in data analysis is that the functional form, as well as the parameter values, of the underlying model which should describe a dataset is not known a priori. In these cases some extra uncertainty must be assigned to the extracted parameters of interest due to lack of exact knowledge of the functional form of the model. A method for assigning an appropriate error is presented. The method is based on considering the choice of functional form as a discrete nuisance parameter which is profiled in an analogous way to continuous nuisance parameters. The bias and coverage of this method are shown to be good when applied to a realistic example.

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years

2026 5

representative citing papers

Defining a Minimum Resolution for Unbinned Analyses

hep-ph · 2026-06-26 · unverdicted · novelty 6.0

The Minimum Resolution Likelihood method defines a fiducial signal region to convert ML-induced systematic effects into statistical uncertainties for unbiased signal strength estimation in collider analyses.

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