Advanced statistical methods to fit nuclear models

TL;DR

This paper introduces advanced statistical techniques, including Gaussian Process Emulation and Markov-chain Monte-Carlo, to enhance parameter estimation and analysis of nuclear models like the Liquid Drop Model.

Contribution

It presents novel application of Gaussian Process Emulation and MCMC methods for efficient nuclear model parameter estimation and likelihood surface analysis.

Findings

Gaussian Process Emulation effectively identifies the area around the global minimum.

MCMC sampling visualizes and analyzes the multidimensional likelihood surface.

Methods improve the accuracy and efficiency of nuclear model fitting.

Abstract

We discuss advanced statistical methods to improve parameter estimation of nuclear models. In particular, using the Liquid Drop Model for nuclear binding energies, we show that the area around the global $\chi^2$ minimum can be efficiently identified using Gaussian Process Emulation. We also demonstrate how Markov-chain Monte-Carlo sampling is a valuable tool for visualising and analysing the associated multidimensional likelihood surface.