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

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

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

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

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

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

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

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

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)

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

Results

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: 0.607 g

Questions