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%.
Cite
@article{arxiv.1811.07050,
title = {Gaussian Process Accelerated Feldman-Cousins Approach for Physical Parameter Inference},
author = {Lingge Li and Nitish Nayak and Jianming Bian and Pierre Baldi},
journal= {arXiv preprint arXiv:1811.07050},
year = {2020}
}
Comments
14 pages, 12 figures, APS April Meeting 2019