Improved data analysis on two-point correlation function with sequential Bayesian method

TL;DR

This paper advances the analysis of two-point correlation functions of B mesons by applying a sequential Bayesian method, improving initial guesses with the Newton method to enhance fitting efficiency and accuracy.

Contribution

It introduces the use of the Newton method to improve initial guesses in Bayesian fitting of two-point correlation functions, leading to faster convergence and better minimization checks.

Findings

Newton method improves initial guess accuracy

Reduces number of iterations in fitting process

Enhances reliability of local vs. global minimum detection

Abstract

We report our progress in data analysis on two-point correlation functions of the $B$ meson using sequential Bayesian method. The data set of measurement is obtained using the Oktay-Kronfeld (OK) action for the bottom quarks (valence quarks) and the HISQ action for the light quarks on the MILC HISQ lattices. We find that the old initial guess for the $\chi^2$ minimizer in the fitting code is poor enough to slow down the analysis somewhat. In order to find a better initial guess, we adopt the Newton method. We find that the Newton method provides a natural test to check whether the $\chi^2$ minimizer finds a local minimum or the global minimum, and it also reduces the number of iterations dramatically.