Gaussian Process Accelerated Feldman-Cousins Approach for Physical Parameter Inference

Abstract

The unified approach of Feldman and Cousins allows for exact statistical inference of small signals that commonly arise in high energy physics. It has gained widespread use, for instance, in measurements of neutrino oscillation parameters in long-baseline experiments. However, the approach relies on the Neyman construction of the classical confidence interval and is computationally intensive as it is typically done in a grid-based fashion over the entire parameter space. In this letter, we propose an efficient algorithm for the Feldman-Cousins approach using Gaussian processes to construct confidence intervals iteratively. We show that in the neutrino oscillation context, one can obtain confidence intervals 5 times faster in one dimension and 10 times faster in two dimensions, while maintaining an accuracy above 99.5%.

arXiv comment14 pages, 12 figures, APS April Meeting 2019

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Cited by in corpus (6)

• Inclusive cross section measurements in final states with and without protons for charged-current ${\nu }_{\mu }$-Ar scattering in MicroBooNE
• First simultaneous measurement of differential muon-neutrino charged-current cross sections on argon for final states with and without protons using MicroBooNE data
• Monte Carlo method for constructing confidence intervals with unconstrained and constrained nuisance parameters in the NOvA experiment
• Measurement of three-dimensional inclusive muon-neutrino charged-current cross sections on argon with the MicroBooNE detector
• An importance sampling method for Feldman-Cousins confidence intervals
• Log Gaussian Cox Process Background Modeling in High Energy Physics