Table of Contents
Name | Direction | Type | Default | Description |
---|---|---|---|---|
DirectBeamRuns | Input | int list | Run number of the signal run to use | |
Attenuators | Input | int list | Number of attenuators for each run | |
TOFRange | Input | dbl list | 10000,35000 | TOF range to use |
SignalPeakPixelRange | Input | int list | 150,160 | Pixel range defining the data peak |
SignalBackgroundPixelRange | Input | int list | 147,163 | Pixel range defining the background |
LowResolutionPixelRange | Input | int list | 94,160 | Pixel range defining the region to use in the low-resolution direction |
IncidentMedium | Input | string | Air | Name of the incident medium |
FrontSlitName | Input | string | S1 | Name of the front slit |
BackSlitName | Input | string | Si | Name of the back slit |
TOFSteps | Input | number | 500 | TOF step size |
ScalingFactorFile | Input | string | Mandatory | Allowed values: [‘cfg’] |
Used in the Liquids Reflectometer reduction at the SNS, this algorithm computes the absolute scaling factors for the data sets that we are going to stitch together.
The algorithm runs through a sequence of direct beam data sets to extract scaling factors. The method was developed by J. Ankner (ORNL).
As we loop through, we find matching data sets with the only difference between the two is an attenuator. The ratio of those matching data sets allows use to rescale a direct beam run taken with a larger number of attenuators to a standard data set taken with tighter slit settings and no attenuators.
The normalization run for a data set taken in a given slit setting configuration can then be expressed in terms of the standard 0-attenuator data set with:
Here’s an example of runs and how they are related to F.
Categories: Algorithms | Reflectometry\SNS
Python: LRScalingFactors.py