July 29, 2019 to August 2, 2019
Northeastern University
US/Eastern timezone

Combined Neyman-Pearson Chi-square: an improved approximation to the Poisson-likelihood chi-square

Jul 30, 2019, 2:20 PM
Shillman 425 (Northeastern University)

Shillman 425

Northeastern University

Oral Presentation Computing, Analysis Tools, & Data Handling Computing, Analysis Tools, & Data Handling


Dr Xiangpan Ji (Brookhaven National Lab)


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.

Primary author

Dr Xiangpan Ji (Brookhaven National Lab)


Chao Zhang (Brookhaven National Laboratory) Hanyu Wei Wenqiang Gu (Brookhaven National Laboratory (US)) Xin Qian (Brookhaven National Laboratory)

Presentation materials