论文标题
通过顺序贝叶斯方法对两点相关函数的改进数据分析
Improved data analysis on two-point correlation function with sequential Bayesian method
论文作者
论文摘要
我们使用顺序贝叶斯方法报告了$ b $ erson的两点相关功能的数据分析进度。测量数据集使用底部夸克（Valence Quarks）的Oktay-Kronfeld（OK）操作获得，并在MILC HISQ晶格上进行的Light Quarks的HISQ动作。我们发现，在拟合代码中，$χ^2 $最小化的旧初始猜测足以在某种程度上减慢分析。为了找到更好的初始猜测，我们采用了牛顿方法。我们发现，牛顿方法提供了一种自然测试，以检查$χ^2 $最小化器是否找到了局部最小值或全球最小值，并且还大大减少了迭代次数。
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 $χ^2$ 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 $χ^2$ minimizer finds a local minimum or the global minimum, and it also reduces the number of iterations dramatically.
