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FindPeakBackground v1¶

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Summary ¶

Separates background from signal for spectra of a workspace.

Properties ¶

Name	Direction	Type	Default	Description
InputWorkspace	Input	MatrixWorkspace	Mandatory	Name of input MatrixWorkspace that contains peaks.
WorkspaceIndex	Input	number	Optional	workspace indices to have peak and background separated. No default is taken.
SigmaConstant	Input	number	1	Multiplier of standard deviations of the variance for convergence of peak elimination. Default is 1.0.
FitWindow	Input	dbl list		Optional: enter a comma-separated list of the minimum and maximum X-positions of window to fit. The window is the same for all indices in workspace. The length must be exactly two.
BackgroundType	Input	string	Linear	Type of Background. Allowed values: [‘Flat’, ‘Linear’, ‘Quadratic’]
OutputWorkspace	Output	TableWorkspace	Mandatory	The name of the TableWorkspace in which to store the background found for each index. Table contains the indices of the beginning and ending of peak and the estimated background coefficients for the constant, linear, and quadratic terms.

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.Straasø.., D. Mueter, H. O.Sørensen..and J. Als-Nielsen Strass J. Appl. Cryst. (2013). 46, 663-671

Categories: AlgorithmIndex | Utility\Calculation