Speaker
Dr
Xiangpan Ji
(Brookhaven National Lab)
Description
We present a new method to approximate the widely-used Poisson-likelihood chi-square using a linear combination of Neyman's and Pearson's chi-squares, namely ``combined Neyman-Pearson chi-square'' (CNP). Through analytical derivation and toy model simulations, we show that CNP leads to a significantly smaller bias on the best-fit normalization parameter compared to that using either Neyman's or Pearson's chi-square. When the computational cost of using the Poisson-likelihood chi-square is high, CNP provides a good alternative given its natural connection to the covariance matrix formalism.
Author
Dr
Xiangpan Ji
(Brookhaven National Lab)
Co-authors
Chao Zhang
(Brookhaven National Laboratory)
Hanyu Wei
Wenqiang Gu
(Brookhaven National Laboratory (US))
Xin Qian
(Brookhaven National Laboratory)
