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Monte Carlo method for constructing confidence intervals with unconstrained and constrained nuisance parameters in the NOvA experiment
NOvA Collaboration Meyer, Holger Muether, Mathew Solomey, Nickolas Roy, P.
NOvA Collaboration
Meyer, Holger
Muether, Mathew
Solomey, Nickolas
Roy, P.
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2025-02-05
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Article
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Analysis and statistical methods,Computing (architecture, farms, GRID for recording, storage, archiving, and distribution of data)
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Citation
M.A. Acero et al 2025 JINST 20 T02001
Abstract
Measuring observables to constrain models using maximum-likelihood estimation is fundamental to many physics experiments. Wilks' theorem provides a simple way to construct confidence intervals on model parameters, but it only applies under certain conditions. These conditions, such as nested hypotheses and unbounded parameters, are often violated in neutrino oscillation measurements and other experimental scenarios. Monte Carlo methods can address these issues, albeit at increased computational cost. In the presence of nuisance parameters, however, the best way to implement a Monte Carlo method is ambiguous. This paper documents the method selected by the NOvA experiment, the profile construction. It presents the toy studies that informed the choice of method, details of its implementation, and tests performed to validate it. It also includes some practical considerations which may be of use to others choosing to use the profile construction. © 2025 IOP Publishing Ltd and Sissa Medialab. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
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This is an open access article under the CC BY license.
Publisher
Institute of Physics
Journal
Journal of Instrumentation
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Archival Collection
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17480221