DetectorEfficiencyCor v1¶

Summary¶

This algorithm adjusts the binned data in a workspace for detector efficiency, calculated from the neutrons’ kinetic energy, the gas filled detector’s geometry and gas pressure. The data are then multiplied by \(k_i/k_f\)

Properties¶

Description¶

The probability of neutron detection by each detector in the workspace is calculated from the neutrons’ kinetic energy, angle between their path and the detector axis, detector gas pressure, radius and wall thickness. The detectors must be cylindrical and their ³He partial pressure, wall thickness and radius are attached to the instrument stored in the input workspace, The first parameter is in atmospheres and the last two in metres. That workspace then needs to be converted so that its X-values are in units of energy transfer, e.g. using the ConvertUnits v1 algorithm.

To estimate the true number of neutrons that entered the detector the counts in each bin are divided by the detector efficiency of that detector at that energy. The efficiency iteslef is calculated from the formula, tabulated within the algorithm.

The numbers of counts are then multiplied by the value of \(k_i/k_f\) for each bin. In that formula \(k_i\) is the wavenumber a neutron leaving the source (the same for all neutrons) and \(k_f\) is the wavenumber on hitting the detector (dependent on the detector and energy bin). They’re calculated, in angstrom^-1, as

where \(E_i\) and \(\Delta E\) are energies in meV, the initial neutron kinetic energy and the energy lost to the sample respectively.

Note: it is not possible to use this algorithm to correct for the detector efficiency alone. One solution to this is to divide the output of the algorithm by \(k_i/k_f\) calculated as above.

Usage¶

Example - Correct detectors efficiency:

# Simulates Load of a workspace with all necessary parameters #################
detWS = CreateSimulationWorkspace(Instrument='MAR',BinParams=[-50,2,50],UnitX='DeltaE')
detWS.dataE(0)[range(0,50)]=1
AddSampleLog(detWS,LogName='Ei',LogText='52.',LogType='Number');

# Correct detectors efficiency
corWS = DetectorEfficiencyCor(detWS)
corWS = CorrectKiKf(corWS,EMode='Direct')

#
# Look at sample results:
print('part of the corrected workspace:')
for i in range(15,30):
   print('{} {} {:.11f} {} {:.11f}'.format(detWS.readX(0)[i],detWS.readY(0)[i],corWS.readY(0)[i],detWS.readE(0)[i],corWS.readE(0)[i]))

Output:

part of the corrected workspace:
-20.0 1.0 1.11489184233 1.0 1.11489184233
-18.0 1.0 1.12451654494 1.0 1.12451654494
-16.0 1.0 1.13460358926 1.0 1.13460358926
-14.0 1.0 1.14519004998 1.0 1.14519004998
-12.0 1.0 1.15631723061 1.0 1.15631723061
-10.0 1.0 1.16803129778 1.0 1.16803129778
-8.0 1.0 1.18038403500 1.0 1.18038403500
-6.0 1.0 1.19343374325 1.0 1.19343374325
-4.0 1.0 1.20724632323 1.0 1.20724632323
-2.0 1.0 1.22189658402 1.0 1.22189658402
0.0 1.0 1.23746983599 1.0 1.23746983599
2.0 1.0 1.25406384358 1.0 1.25406384358
4.0 1.0 1.27179123770 1.0 1.27179123770
6.0 1.0 1.29078252032 1.0 1.29078252032
8.0 1.0 1.31118984059 1.0 1.31118984059

Categories: AlgorithmIndex | CorrectionFunctions\EfficiencyCorrections | Inelastic\Corrections

Source¶

C++ header: DetectorEfficiencyCor.h

C++ source: DetectorEfficiencyCor.cpp