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Time-of-Flight Powder Diffraction Calibration¶

Data Required
Calculating Calibration
Testing your Calibration
Expanding on detector masking
Creating detector grouping
Adjusting the Instrument Definition

Data Required ¶

To get a good calibration you will want good statistics with calibration data. For most of the calibration algorithms, this means having enough statistics in a single pixel to fit individual peaks or to cross-correlate data. Some of the calibration algorithms also require knowing the ideal positions of peaks in d-spacing. For these algorithms, it is not uncommen to calibrate the instrument, refine the results using a Rietveld program, then using the updated peak positions to calibrate again. Often the sample selected will be diamond.

Calculating Calibration ¶

Relative to a specific spectrum ¶

This technique of calibration uses a reference spectrum to calibrate the rest of the instrument to. The main algorithm that does this is GetDetectorOffsets whose InputWorkspace is the OutputWorkspace of CrossCorrelate. Generically the workflow is

Load the calibration data
Convert the X-Units to d-spacing using ConvertUnits. The “offsets” calculated are relative reference spectrum’s geometry so using AlignDetectors will violate the assumptions for other algorithms used with time-of-flight powder diffraction and give the wrong results for focused data.
Run Rebin to set a common d-spacing bin structure across all of the spectra, you will need fine enough bins to allow fitting of your peak. Whatever you choose, make a note of it you may need it later.
Take a look at the data using the SpectrumViewer or a color map plot, find the workspace index of a spectrum that has the peak close to the known reference position. This will be the reference spectra and will be used in the next step.
Cross correlate the spectrum with CrossCorrelate, enter the workspace index as the ReferenceSpectra you found in the last step.
Run GetDetectorOffsets, the InputWorkspace if the output from CrossCorrelate. Use the rebinning step you made a note of in step 3 as the step parameter, and DReference as the expected value of the reference peak that you are fitting to. XMax and XMin define the window around the reference peak to search for the peak in each spectra, if you find that some spectra do not find the peak try increasing those values.
The output is an OffsetsWorspace. See below for how to save, load, and use the workspace.

Using known peak positions ¶

These techniques require knowing the precise location, in d-space, of diffraction peaks and benefit from knowing more. GetDetOffsetsMultiPeaks and PDCalibration are the main choices for this. Both algorithms fit individual peak positions and use those fits to generate the calibration information.

The workflow for GetDetOffsetsMultiPeaks is identical to that of GetDetectorOffsets without the cross-correlation step (5). The main difference in the operation of the algorithm is that it essentially calculates an offset from each peak then calculates a weighted average of those offsets for the individual spectrum.

The workflow for PDCalibration differs significantly from that of the other calibration techniques. It requires the data to be in time-of-flight, then uses either the instrument geometry, or a previous calibration, to convert the peak positions to time-of-flight. The individual peaks fits are then used to calculate \(DIFC\) values directly. The benefit of this method, is that it allows for calibrating starting from a “good” calibration, rather than returning back to the instrument geometry. The steps for using this are

Load the calibration data
Run PDCalibration with appropriate properties
The OutputCalibrationTable is a TableWorkspace. See below for how to save, load, and use the workspace.

Workflow algorithms ¶

CalibrateRectangularDetectors will do most of the workflow for you, including applying the calibration to the data. While its name suggests it is only for a particular subset of detector types, it is not. It has many options for selecting between GetDetectorOffsets and GetDetOffsetsMultiPeaks.

Saving and Loading Calibration ¶

There are two basic formats for the calibration information. The legacy ascii format is described in CalFile. The newer HDF5 version is described alongside the description of calibration table.

Saving and loading the HDF5 format is done with SaveDiffCal and LoadDiffCal.

Saving and loading the legacy format is done with SaveCalFile and LoadCalFile. This can be converted from an OffsetsWorkspace to a calibration table using ConvertDiffCal.

Testing your Calibration ¶

The first thing that should be done is to convert the calibration workspace (either table or OffsetsWorkspace to a workspace of \(DIFC\) values to inspect using the instrument view. This can be done using CalculateDIFC. The values of \(DIFC\) should vary continuously across the detectors that are close to each other (e.g. neighboring pixels in an LPSD).

You will need to test that the calibration managed to find a reasonable calibration constant for each of the spectra in your data. The easiest way to do this is to apply the calibration to your calibration data and check that the bragg peaks align as expected.

Load the calibration data using Load
Run AlignDetectors, this will convert the data to d-spacing and apply the calibration. You can provide the calibration using the CalibrationFile, the CalibrationWorkspace, or OffsetsWorkspace.
Plot the workspace as a Color Fill plot, in the spectrum view, or a few spectra in a line plot.

Further insight can be gained by comparing the grouped (after aligning and focussing the data) spectra from a previous calibration or convert units to the newly calibrated version. This can be done using AlignAndFocusPowder with and without calibration information. In the end, a Rietveld refinement is the best test of the calibration.

Expanding on detector masking ¶

While many of the calibration methods will generate a mask based on the detectors calibrated, sometimes additional metrics for masking are desired. One way is to use DetectorDiagnostic. The result can be combined with an existing mask using

BinaryOperateMasks(InputWorkspace1='mask_from_cal', InputWorkspace2='mask_detdiag',
                   OperationType='OR', OutputWorkspace='mask_final')

Creating detector grouping ¶

To create a grouping workspace for SaveDiffCal you need to specify which detector pixels to combine to make an output spectrum. This is done using CreateGroupingWorkspace. An alternative is to generate a grouping file to load with LoadDetectorsGroupingFile.

Adjusting the Instrument Definition ¶

This approach attempts to correct the instrument component positions based on the calibration data. It can be more involved than applying the correction during focussing.

Perform a calibration using CalibrateRectangularDetectors or GetDetOffsetsMultiPeaks. Only these algorithms can export the Diffraction Calibration Workspace required.
Run AlignComponents this will move aspects of the instrument to optimize the offsets. It can move any named aspect of the instrument including the sample and source positions. You will likely need to run this several times, perhaps focussing on a single bank at a time, and then the source and sample positions in order to get a good alignment.
Then either:
- ExportGeometry will export the resulting geometry into a format that can be used to create a new XML instrument definition. The Mantid team at ORNL have tools to automate this for some instruments at the SNS.
- At ISIS enter the resulting workspace as the calibration workspace into the DAE software when recording new runs. The calibrated workspace will be copied into the resulting NeXuS file of the run.

Category: Calibration