TDCRPy 1.9.6

TDCR model

TDCRPy

TDCRPy is a Python code to calculate detection efficiency of a liquid scintillation counter using 3-photomultiplier tubes. The calculation is based on the photo-physical model called of the Triple-to-Double-Coincidence-Ratio method (TDCR) and a Monte-Carlo sampling allowing to adress complexe decay schemes and radionuclide mixtures.

The code is developped and maintained by the BIPM (MIT license).

Installation

TDCRPy requires that the following packages are installed in your Python environement.

pip install importlib.resources configparser numpy tqdm setuptools scipy

or in conda environement:

conda install importlib.resources configparser numpy tqdm setuptools scipy

Then, TDCRPy can be installed.

pip install TDCRPy

To obtain the last version.

pip install TDCRPy --upgrade

The module can be imported in your Python code such as.

import tdcrpy

Test

To run the unit tests of the package:

python -m unittest tdcrpy.test.test_tdcrpy

or (using the coverage package)

coverage run -m unittest tdcrpy.test.test_tdcrpy
coverage report -m

User guide

At first, the TDCRPy module must be imported.

import tdcrpy

TDCRPy()

The main function of the TDCRPy module is TDCRPy() in which the Monte-Carlo Triple-to-Double Coincidence Ratio model is implemented. The computation is made for a given solution containing a radionuclide (or a mixture of radionuclides) rad, a given volume of scintillator V and a given Birks constant kB.

It can operates in two modes:

• In mode="eff", it calculates the efficiency of the TDCR system as a function of a value (or a triplet) of the free parameter(s) L. The measurement data is not used is this mode.
• In mode="res", it calculates the residual of the TDCR model parametrized by a value (or triplet) of the free parameter(s) L and the measurement data TD, TAB, TBC, TAC. This residual is used in the optimisation procedure tdcrpy.TDCRoptimize.eff() that aims to estimate the dection efficiency of the TDCR system (see above).

Also, two configurations can be set:

• In mode2="sym", the symmetry is considered between the 3 photomultiplier tubes (PMTs). Here, the free parameter L is a scalar and only the global TDCR value TD is used as measurement data.
• In mode2="asym", a possible asymmetry between the 3 photomultiplier tubes is considered in the model. Here, the free parameter L is a triplet corresponding to each PMTs and only the specific TDCR values TAB, TBC and TAC are used as measurement data.

The parmeter N sets the number of Monte-Carlo trails used for the estimation. Each MC trial corresponds to a simulated radiactive decay.

TDCRPy() is parametrised by the following parameters:

• L is the free parameter(s) in keV^-1^. It represents the number of photoelectrons produced by the PMTs per unit of ionisation energy released in the scintillator during a decay without considering the scintillation quenching. It is of type float when mode2="sym" and of type tuple or list when mode2="asym".
• TD (type float) is the quotient of the triple coincidence count rate divided by the logic sum of the double coincidence count rate.
• TAB (type float) is the quotient of the triple coincidence count rate divided by the double coincidence count rate between the channel A and B.
• TBC (type float) is the quotient of the triple coincidence count rate divided by the double coincidence count rate between the channel B and C.
• TAC (type float) is the quotient of the triple coincidence count rate divided by the double coincidence count rate between the channel A and C.
• rad (type string) is the list of radionuclides contained in the solution, e.g. "H-3", "H-3, Co-60".
• pmf_1 (type string) is the probability of each radionuclide contained in rad. If rad = "H-3, Co-60" and pmf_1="0.8, 0.2", the solution contains 80 % of tritium and 20 % of cobalt 60.
• N (type integer) is the number of Monte-Carlo trials corresponding each to a simulated nuclear decay in the scintillator. Monte-Carlo calculation is not used in the case of pure beta emitting radionuclides where the analytical model is implemented.
• kB (type float) is the Birks constant caracterizing the scintillator. It is espressed in cm/keV.
• V (type float) is the volume in ml of the scintillator.
• mode sets the type of output: mode="res" to return the residual, mode="eff" to return efficiencies.
• mode2 sets whether the TDCR system has to be considered as symetrical by the model mode2="sym" or not symetrical mode2="asym".
• Display (type boolean) is an optional parameter set by default, Display=False. If Display=True, detailed on the simulated decays are displayed.
• barp (type boolean) is an optional parameter set by default, barp=False. If barp=True, the progression bar of the calculation is displayed.

In mode="eff", TDCRPy() returns a tuple composed of:

• the estimation of the counting efficiency of single events S,
• the standard uncertainty of the estimation of S,
• the estimation of the counting efficiency of double coincidences D,
• the standard uncertainty of the estimation of D,
• the estimation of the counting efficiency of triple coincidences T,
• the standard uncertainty of the estimation of T,

Example:

import tdcrpy

L = 1.5
TD = 0.977667386529166
TAB = 0.992232838598821
TBC = 0.992343419459002
TAC = 0.99275350064608
Rad="Co-60"
pmf_1="1"
N = 100
kB =1.0e-5
V = 10
mode = "eff"
mode2 = "sym"

result = tdcrpy.TDCRPy.TDCRPy(L, TD, TAB, TBC, TAC, Rad, pmf_1, N, kB, V, mode, mode2)

print("efficiency S = ", round(result[0],4), "+/-", round(result[1],4))
print("efficiency D = ", round(result[2],4), "+/-", round(result[3],4))
print("efficiency T = ", round(result[4],4), "+/-", round(result[5],4))

efficiency S =  0.9875 +/- 0.007
efficiency D =  0.9903 +/- 0.0072
efficiency T =  0.9749 +/- 0.0121

In mode="res", TDCRPy() returns the residuals R (type float) of the TDCR model for the given value of the free parameter L.

Example:

import tdcrpy

L = 1.5
TD = 0.977667386529166
TAB = 0.992232838598821
TBC = 0.992343419459002
TAC = 0.99275350064608
Rad="Co-60"
pmf_1="1"
N = 100
kB =1.0e-5
V = 10
mode = "res"
mode2 = "sym"

result = tdcrpy.TDCRPy.TDCRPy(L, TD, TAB, TBC, TAC, Rad, pmf_1, N, kB, V, mode, mode2)

print("R = ",result)

R =  9.72238771394384e-06

When a triplet of free parameters is considered, L is of type tuple and mode2 = "asym". It must be noted that the calculation time is three times higher than in the symetrical model.

Example:

import tdcrpy

L = (1.5, 1.2, 1.4)
TD = 0.977667386529166
TAB = 0.992232838598821
TBC = 0.992343419459002
TAC = 0.99275350064608
Rad="Co-60"
pmf_1="1"
N = 100
kB =1.0e-5
V = 10
mode = "res"
mode2 = "asym"

result = tdcrpy.TDCRPy.TDCRPy(L, TD, TAB, TBC, TAC, Rad, pmf_1, N, kB, V, mode, mode2)

print("R = ",result)

R =  1.4353680366705997e-05

By default, the Monte-Carlo model is not applied for the following list radionuclides when used solely (not in a mixture): ^3^H, ^14^C, ^35^S, ^45^Ca, ^63^Ni, ^89^Sr, ^90^Sr, ^99^Tc, ^147^Pm, ^241^Pu. In the latter cases, the analytical model is applied and the calculation time does not dependent to the parameter N.

Advance settings

An advanced setting can be configured in the config.toml file for functions TDCRPy.TDCRPy() and TDCRoptimize.eff(). In this file:

• By default Y = True so that the analytical model is applied for solution containing only pure beta emitting radionuclides. If you would like to apply the MC calculation also for these nuclides, set Y = False.
• The list of radionuclides for which the analytical model is applied is defined by default such as radListPureBeta = H-3, C-14, S-35, Ca-45, Ni-63, Sr-89, Sr-90, Tc-99, Pm-147, Pu-241.
• The number of bins to discretize the linear energy space for quenching calculation for the radionuclides listed above has been set to induce an error from numerical approximation below 10^-4^. Thus the parameter nE = 7000, 1000, 1000, 500, 2000, 500, 200, 500, 1000, 7000.
• In the case of Monte-Carlo calculation, the number of bins to discretize the linear energy space for quenching calculation can be adjusted. nE_electron and nE_alpha parameters for respectively electrons and alpha particles are respectiveley set by default such as nE_electron = 1000 and nE_alpha = 1000. These values ensure an error on quenched energy estimation from numerical approximation below 10^-3^.
• By default the calculation is set for Ultima-Gold cocktail mixed with a small amount of aqueous solution. You can adapt for a specific scintillator by changing the density (default density=0.96), the mean charge number Z (default Z=5.2) and the mean mass number A (default A=11.04) of the scintillator.
• To process photoelectric interactions, the atomic concentration of the scintillator is spectified. By default pH = 0.578772, pC = 0.338741, pN = 0.000302, pO = 0.082022, pP = 0.000092, pCl = 0.000071
• A correction due reverse micelles in the scintillator is implemented. The diameter of the micelles and the fraction of aqueous solution is specified. By defaut diam_micelle = 2, fAq = 0.1
• To optimize the speed of the Monte-Carlo calculation, a spline interpolation on precalculated quenched energy is applied. The parameter depthSpline (default depthSpline = 5) sets the number of bins on each side of the energy point on which the interpolation is applied. The parameter Einterp (default Einterp = 1) set the energy (in keV) above which the interpolation is applied.

TDCRoptimize.eff()

An optimization procedure TDCRoptimize.eff() is proposed to estimate the counting efficiency of a TDCR system. It approximates the free parameter(s) L in order to minimize the residual estimated by TDCRPy() for a given set of measurement data. In mode2 = "sym", the golden section search technique is applied with a maximum number of iterations of 20. In mode2 = "asym", the Nelder-Mead technique is applied with a maximum number of iterations of 20.

TDCRoptimize.eff() is parametrised by the following parameters:

• TD (type float) is the quotient of the triple coincidence count rate divided by the logic sum of the double coincidence count rate.
• TAB (type float) is the quotient of the triple coincidence count rate divided by the double coincidence count rate between the channel A and B.
• TBC (type float) is the quotient of the triple coincidence count rate divided by the double coincidence count rate between the channel B and C.
• TAC (type float) is the quotient of the triple coincidence count rate divided by the double coincidence count rate between the channel A and C.
• rad (type string) is the list of radionuclides contained in the solution, e.g. "H-3", "H-3, Co-60".
• pmf_1 (type string) is the probability of each radionuclide contained in rad. If rad = "H-3, Co-60" and pmf_1="0.8, 0.2", the solution contains 80 % of tritium and 20 % of cobalt 60.
• kB (type float) is the Birks constant caracterizing the scintillator. It is espressed in cm/keV.
• V (type float) is the volume in ml of the scintillator.
• mode2 sets whether the TDCR system has to be considered symetrical mode2="sym" or not symetrical mode2="asym".
• N (type integer) is the number of Monte-Carlo trials corresponding each, to a simulated nuclear decay in the scintillator. Monte-Carlo calculation is not used in the case of pure beta emitting radionuclides where the analytical model is implemented. It is an optional parameter set by default at N = 10000.
• L (type float) initial guess of the free parameter(s) in keV^-1^. It represents the number of photoelectrons produced by the PMTs per unit of ionisation energy released in the scintillator during a decay and in the abscence of scintillation quenching. It is set by default at L = 1.

TDCRoptimize.eff() returns:

• the global free parameter L,
• the free parameters related to each channel (L_A, L_B, L_C),
• the estimation of the counting efficiency of single events S,
• the standard uncertainty of the estimation of S,
• the estimation of the counting efficiency of double coincidences D,
• the standard uncertainty of the estimation of D,
• the estimation of the counting efficiency of triple coincidences T,
• the standard uncertainty of the estimation of T.

Example with mode2 = "sym":

import tdcrpy

TD = 0.977667386529166
TAB = 0.992232838598821
TBC = 0.992343419459002
TAC = 0.99275350064608
Rad="Co-60"
pmf_1="1"
N = 250
kB =1.0e-5
V = 10
mode2 = "sym"

result = tdcrpy.TDCRoptimize.eff(TD, TAB, TBC, TAC, Rad, pmf_1, kB, V, mode2, N=N)

print("Global free parameter = \t", round(result[0],4), " keV-1")
print("Free parameter (PMT A) = \t", round(result[1][0],4) , " keV-1")
print("Free parameter (PMT B) = \t", round(result[1][1],4) , " keV-1")
print("Free parameter (PMT C) = \t", round(result[1][2],4) , " keV-1")
print("efficiency S = \t", round(result[2],4), "+/-", round(result[3],4))
print("efficiency D = \t", round(result[4],4), "+/-", round(result[5],4))
print("efficiency T = \t", round(result[6],4), "+/-", round(result[7],4))

Global free parameter = 	 1.3061  keV-1
Free parameter (PMT A) = 	 1.3061  keV-1
Free parameter (PMT B) = 	 1.3061  keV-1
Free parameter (PMT C) = 	 1.3061  keV-1
efficiency S = 	 0.979 +/- 0.0067
efficiency D = 	 0.9833 +/- 0.0064
efficiency T = 	 0.963 +/- 0.0096

Example with mode2 = "asym":

import tdcrpy

TD = 0.977667386529166
TAB = 0.992232838598821
TBC = 0.992343419459002
TAC = 0.99275350064608
Rad="Co-60"
pmf_1="1"
N = 250
kB =1.0e-5
V = 10
mode2 = "asym"

result = tdcrpy.TDCRoptimize.eff(TD, TAB, TBC, TAC, Rad, pmf_1, kB, V, mode2, N=N)

print("Global free parameter = \t", round(result[0],4), " keV-1")
print("Free parameter (PMT A) = \t", round(result[1][0],4) , " keV-1")
print("Free parameter (PMT B) = \t", round(result[1][1],4) , " keV-1")
print("Free parameter (PMT C) = \t", round(result[1][2],4) , " keV-1")
print("efficiency S = \t", round(result[2],4), "+/-", round(result[3],4))
print("efficiency D = \t", round(result[4],4), "+/-", round(result[5],4))
print("efficiency T = \t", round(result[6],4), "+/-", round(result[7],4))

Global free parameter = 	 1.3061  keV-1
Free parameter (PMT A) = 	 1.3061  keV-1
Free parameter (PMT B) = 	 1.3061  keV-1
Free parameter (PMT C) = 	 1.3061  keV-1
efficiency S = 	 0.979 +/- 0.0067
efficiency D = 	 0.9833 +/- 0.0064
efficiency T = 	 0.963 +/- 0.0096