PEARLTransfit v2#

Summary#

Version 2 of PEARLTransfit. Reads high-energy neutron resonances from the downstream monitor data on the PEARL instrument, then fits a Voigt function to them to determine the sample temperature. A calibration must be run for each sample pressure. Can be used on a single file, or multiple files, in which case workspaces are summed and the average taken.

Properties#

Name

Direction

Type

Default

Description

Files

Input

list of str lists

Mandatory

File paths or comma separated run numbers for target runs (numors). Must be detector scans. Allowed extensions: [‘.raw’, ‘.s0x’, ‘.nxs’]

FoilType

Input

string

Hf01

Type of foil included with the sample. Allowed values: [‘Hf01’, ‘Hf02’, ‘Ta10’, ‘Irp6’, ‘Iro5’, ‘Iro9’]

Calibration

Input

boolean

False

Calibration flag, default is False in which case temperature measured

InputCalibrationParameters

Input

TableWorkspace

Table with fit parameters for ‘PEARLTransVoigt’ function: ‘Position’, ‘LorentzianFWHM’, ‘GaussianFWHM’, ‘Amplitude’, ‘Bg0’’Bg1’, ‘Bg2’. The calculated temperature is added in non-calibration runs. This property is mandatory when calibration is set to False. If a ‘InputCalibrationParameters’ is used in calibration runs: ‘Bg0’, ‘Bg1’ and ‘Bg2’ will be used for background estimation

ReferenceTemp

Input

number

290

Enter reference temperature in K

EstimateBackground

Input

boolean

True

Estimate background parameters from data.

CreateDebugTable

Input

boolean

False

Create an output table with debug parameters in non calibration runs

Output

Input

string

Output basename on ADS. If empty, S_fit will be the basename for calibration runs and T_fit for non-calibration, adding the foil type and run numbers as suffixes.

Description#

PEARL high-pressure, high-temperature measurements rely on the use neutron transmission data to determine the sample temperature. Typically Hf foil is included with the sample, though others can be used, provided they have well characterised resonances. Transfit is an algorithm which reads high-energy neutron resonances from the downstream monitor data on the PEARL instrument. It fits a Voigt function to them to determine the sample temperature. It is broadly similar to the Fortran77 version originally implemented on PEARL through OpenGenie. As currently coded – this is only appropriate for use on the PEARL instrument.

Full details of how the temperature information is extracted from the peak shape parameters, can be found in the references below. Essentially, there are two components contributing to the peak shape, the Gaussian and Lorentzian. First, the calibration run must be performed to fit the instrument contributions to the line-shape, and account for any other effects to the line-shape at a given sample pressure. After this, only the Gaussian component is fitted to extract the temperature. Note that a recalibration is required for each given sample pressure.

Version 2#

For version 1 of this algorithm, see PEARLTransfit-v1.

In version 2 of this algorithm:

  • The Output Workspace and Parameters Table are created as output properties and not directly extracted from the fit, this preserves workspace history.

  • The Output property is a string that sets the basename for outputs in the ADS.

  • There are no inputs for background parameter estimation, these can be provided in the InputCalibrationParameters.

  • There is no rebin in constant energy.

  • In non-calibration fits, a debug table with information about sample temperature can be output if CreateDebugTable is set to True. The output table also contains the computed sample temperature.

If no InputCalibrationParameters is provided on a calibration run, the background parameters will be estimated from the data. This generates an output with the suffix _Parameters on the ADS containing, among others, background estimation parameters. This table can be used on successive calls to input background parameters. If custom background parameters need to be provided in a calibration run, a table workspace with the appropriate parameters can be created from the following script.

def create_input_calibration_table(bg0, bg1, bg2, name="InputCalibrationParameters"):
    table = CreateEmptyTableWorkspace(OutputWorkspace=name)

    table.addColumn("str","Name")
    table.addColumn("float","Value")

    for idx, bg in enumerate([bg0, bg1, bg2]):
        table.addRow({"Name":f"Bg{idx}", "Value": bg})
    return table

table = create_input_calibration_table(10, 0.01, 0.000004)
cal_ws, params_table = PEARLTransfit(Files='PEARL00112777', FoilType="Hf01", Calibration=True, InputCalibrationParameters=table, Output='pearl_cal')

See script below for usage.

from mantid.simpleapi import *

# Calibration
cal_ws, params_table = PEARLTransfit(Files='PEARL00112777', FoilType="Hf01", Calibration=True, EstimateBackground=True, Output='pearl_cal')

# Using results from calibration
non_cal_ws, params_table, debug_table= PEARLTransfit(Files='PEARL00112777', FoilType="Hf01", Calibration=False, InputCalibrationParameters=params_table, CreateDebugTable=True)

# Debug table contains temperature and fitting information.
row_temp_debug = debug_table.row(debug_table.rowCount()-1)
print(f"Sample Temperature (K) {row_temp_debug['Value']:.3f} +- {row_temp_debug['Error']:.3f}")
# Fit Parameters table also includes calculated temperature
row_temp_fit_table = params_table.row(params_table.rowCount()-1)
print(f"Sample Temperature (K) {row_temp_fit_table['Value']:.3f} +- {row_temp_fit_table['Error']:.3f}")

Output:

Sample Temperature (K) 279.760 +- 14.717
Sample Temperature (K) 279.760 +- 14.717

References#

‘Temperature measurement in a Paris-Edinburgh cell by neutron resonance spectroscopy’ - Journal Of Applied Physics 98, 064905 (2005) ‘Remote determination of sample temperature by neutron resonance spectroscopy’ - Nuclear Instruments and Methods in Physics Research A 547 (2005) 601-615

Categories: AlgorithmIndex | Diffraction\Fitting

Source#

Python: PEARLTransfit2.py