Speaker
Description
Feldman-Cousins method is a unified approach to create frequentist confidence intervals near physical limits or with low statistics. It has been widely used in oscillation parameter inference for neutrino experiments. However, the Feldman-Cousins method is usually computationally expensive, on the order of tens of millions of CPU hours. In this work, we propose an iterative method using Gaussian Process to efficiently estimate a frequentist confidence contour for the neutrino oscillation parameters. We show that the Gaussian Process enhanced method significantly reduces the computational cost while producing the same result as the standard Feldman-Cousins method.
Mini-abstract
Gaussian Process boosts neutrino oscillation parameter inference with Feldman-Cousins approach