PROBABILISTIC UNCERTAINTY ESTIMATOR

• US 20090292493A1
• Filed: 03/19/2007
• Published: 11/26/2009
• Est. Priority Date: 03/17/2006
• Status: Active Grant

• Current Assignee: Mirion Technologies Incorporated (American Capital Limited)
• Original Assignee: Canberra Industries, Inc. (American Capital Limited)
• Inventors: Atrashkevich, Valery, Bronson, Frazier
• Granted Patent: US 8,335,670 B2
• Time in Patent Office: Days
• US Class Current: 702/85
• CPC Class Codes: G01T 7/005 calibration techniques stabilization of spectrometer G01T1/40

Abstract

The present invention computes the uncertainty in the calibration factor or efficiency of a radiation sensor for that portion of the uncertainty arising from imprecise knowledge of the exact measurement conditions. This is accomplished in one aspect of the invention, by accurately defining a mathematical model of the sensor, the sample, and other items affecting the efficiency; then defining the default or expected or normal dimensions or values of each of the parameters in the mathematical model; then defining which of the values or parameters in the mathematical model are variables; for each variable parameter defining the range of variation and the shape of the distribution of those variable parameters; randomly selecting a value for each of the variable parameters in the model, using distribution shape and limits to create a mathematical model of one possible variation of source-detector measurement configuration; using this mathematical model and dimensions to compute the efficiency of the defined source-detector measurement configuration; repeating this random selection process a large number of times; and then computing the mean and standard deviation describing the uncertainty in that efficiency. In a further aspect of the invention, all of the preceding is done while using the mathematical model to compute the efficiency for each of a several energies, in order to evaluate the efficiency versus energy response of the measurement apparatus, and the uncertainty versus energy response of the apparatus.

Claims

1. A method of determining the calibration factor uncertainty for quantitative radiation measurements, comprising:

a) defining mathematical models of sensors, samples, and other items affecting said calibration factor;

b) defining or assuming expected or normal dimensions or values of each parameter in said mathematical model;

c) defining or assuming which values or parameters in said mathematical model are variables;

d) defining or assuming parameters representing limits of each said variable in said mathematical model;

e) defining or assuming parameters representing shape of parameter variability within said limits of each said variable in said mathematical model;

f) selecting statistically random values, consistent with said limits and shapes for each variable parameter in each said mathematical model;

g) using said mathematical model and dimensions to compute calibration factors or efficiencies for conditions described in said mathematical model in step f);

h) repeating steps f) and g) large numbers of times; and

i) computing various parameters describing said calibration factor values computed in step h), such as mean and standard deviation.

2. A method of evaluating calibration factor uncertainty for quantitative radiation measurements using spectroscopic detectors, using one or more energy values comprising:

a) defining mathematical models of sensors, samples, and other items affecting said calibration factor;

b) defining or assuming expected or normal dimensions or values of each parameter in said mathematical model;

c) defining or assuming which values or parameters in said mathematical model are variables;

d) defining parameters representing limits of each said variable in said mathematical model;

e) defining or assuming parameters representing shape of parameter variability within said limits of each said variable in said mathematical model;

f) selecting statistically random values, consistent with said limits and shapes for each variable parameter in each said mathematical model;

g) using said mathematical model and dimensions to compute calibration factors or efficiencies for conditions described in said mathematical model in step f) for one or more specified energies;

h) repeating steps f) and g) large numbers of times; and

i) computing various parameters for one or more energies describing said calibration factor values computed in step h), such as mean and standard deviation.