.. algorithm:: .. summary:: .. relatedalgorithms:: .. properties:: Description ----------- Algorithm written using the paper referenced below which has a very good description. This algorithm estimates the background level and separates the background from signal data in a Poisson-distributed data set by statistical analysis. For each iteration, the bins/points with the highest intensity value are eliminated from the data set and the sample mean and the unbiased variance estimator are calculated. Convergence is reached when the absolute difference between the sample mean and the sample variance of the data set is within k standard deviations of the variance, the default value of k being 1. The k value is called ``SigmaConstant`` in the algorithm input. References ---------- **Objective algorithm to separate signal from noise in a Poisson-distributed pixel data set** by T. |Straaso|, D. Mueter, H. O. |Sorensen| and J. Als-Nielsen Strass `J. Appl. Cryst. (2013). 46, 663-671 `__ .. |Straaso| unicode:: Straas U+00F8 .. :ltrim: .. |Sorensen| unicode:: S U+00F8 rensen .. :trim: .. categories:: .. sourcelink::