1. NIST Standard Reference materials for nuclear forensics
Simulating Fallout
NIST Standard Reference materials for nuclear forensics
Sarah Castro

2. The Curve of Binding Energy
What will happen when the explosions come—when a part of new York or Cairo or Adelaide has been hollowed out by a device in the kiloton range? since even a so called fizzle yield could kill a number of thousands of people, how many nuclear detonations can the world tolerate?
John McPhee,
The Curve of Binding Energy

3. Surrogate Post- detonation urban debris
ISSUE
Surrogate Post- detonation urban debris
Currently, there is no existing standard reference material that represents fresh fission product, post detonation nuclear fallout for an urban environment
Demand in the nuclear forensics community
Certified or Standard Reference Materials can be used to calibrate AND validate laboratory methods

4. Use
Specific purpose
Enable method development, validation, testing, and preparedness in the nuclear forensics community
Labs (FBI, etc.) able to legally and scientifically demonstrate efficacy and precision of analysis methods
Contributes to legal defensibility of real-world measurements
Used for exercises and blind tests of laboratory capabilities

5. MATERIAL
SPUD COMPOSITION
Surrogate urban debris material that mimics “rubble” after a detonation
Vitrified mix of cement, concrete, and steel
Two types of samples analyzed
Doped with natural U
Doped with 22% U-235

6. Material
Characterization
In order to be useful, SPUD needs to be very well-characterized and consistent across batches
Ongoing joint NIST/FBI/AFIT effort uses micro X-ray fluorescence with principal component analysis (among other techniques) to determine material content and heterogeneity

7. Micro-xrf Fluorescence Incident radiation ionizes atom
Analysis techniques
Micro-xrf
Fluorescence
Incident radiation ionizes atom
Atom ejects electron from inner shell
Outer shell electron fills gap
Atom emits characteristic x-ray
Other Factors
Anode composition
Infinite thickness

8. principal component analysis
Analysis Techniques
principal component analysis
Rotate data for most efficient presentation of how each measurement of a sample or set of samples relates to every other measurement
Data set reduction with minimal information loss
IDs variables that account for greatest variance
Algorithm
Center/scale data (plot centers at origin, data with different units considered equally)
Find eigenvectors of covariance matrix (new axes, chosen for relationship strength)
Order eigenvectors by decreasing value of eigenvalues
Apply eigenvectors to centered/scaled data = PCs

9. Experiment Random Sampling 10000 points for SPUD
Process
Experiment
Random Sampling
10000 points for SPUD
Excel macro used to select random points on sample surface
Accounts for variance in sample
Stability analysis
10000 measurements in 1 location
Accounts for variance in measurements not caused by variance in the sample (for instance, machine error)

10. Statistical Analysis of Intensity Values
Process
Statistical Analysis of Intensity Values
Average, Standard Deviation, Relative Standard Deviation, Counting Statistical Error, Skew, Kurtosis
Describe shape of intensity distribution
Also included Atomic Number and Line Energy
PCA performed to determine homogeneity
A perfectly homogeneous sample would have a normal distribution of intensities
Find heterogeneity by identifying deviations from normal

11. Results

12. SPUD fairly consistent between tested samples
Results
SPUD fairly consistent between tested samples
~1% difference between PC model variance
Pending further testing
Behavior of outlier Fe consistent with previous studies performed on our machine
Appearance of “nugget effects” in stability measurement again indicates variance other than sample variance
Fe measurements for these samples may not be accurate
Preliminary minimum sample size: g

13. Questions