Kenneth D. Jarman

Examination of Count-starved Gamma Spectra Using the Method of Spectral Comparison Ratios

We discuss the determination of energy region (bin) boundaries and decision metrics for gamma-ray spectra, acquired using a mid-resolution detector, that are useful for detecting illicit sources at low total counts. The bins are designed to produce the lowest minimum detectable counts using a spectral comparison ratio technique at a given false-positive rate for a specified population of benign-source spectra. Spectra from the benign source population consist of observations taken by a detector on a moving vehicle, as would be obtained during a search for a missing or hidden source. Raw counts in bins are transformed into a vector of background-corrected count differences. Bin boundaries are determined to yield large values of a standardized length of this vector for benign-plus-benchmark sources by applying an optimization technique. The objective function includes penalties for overlap with the spectral features of naturally occurring radioactive materials. We compare estimated minimum detectable count values for such bins applied to depleted uranium and barium-133 sources with those based on gross counting, and we examine the effect of nuisance potassium-, radium- and thorium-dominated sources. Using this methodology, we demonstrate that energy bins may be chosen to be sensitive to special nuclear materials, improving the likelihood of detection in low-count or masked-source searches.

Sequence optimization as an alternative to de novo analysis of tandem mass spectrometry data

MOTIVATION Peptide identification following tandem mass spectrometry (MS/MS) is usually achieved by searching for the best match between the mass spectrum of an unidentified peptide and model spectra generated from peptides in a sequence database. This methodology will be successful only if the peptide under investigation belongs to an available database. Our objective is to develop and test the performance of a heuristic optimization algorithm capable of dealing with some features commonly found in actual MS/MS spectra that tend to stop simpler deterministic solution approaches. RESULTS We present the implementation of a Genetic Algorithm (GA) in the reconstruction of amino acid sequences using only spectral features, discuss some of the problems associated with this approach and compare its performance to a de novo sequencing method. The GA can potentially overcome some of the most problematic aspects associated with de novo analysis of real MS/MS data such as missing or unclearly defined peaks and may prove to be a valuable tool in the proteomics field. We assess the performance of our algorithm under conditions of perfect spectral information, in situations where key spectral features are missing, and using real MS/MS spectral data.

Low Count Anomaly Detection at Large Standoff Distances

Searching for hidden illicit sources of gamma radiation in an urban environment is difficult. Background radiation profiles are variable and cluttered with transient acquisitions from naturally occurring radioactive materials and medical isotopes. Potentially threatening sources likely will be nearly hidden in this noise and encountered at high standoff distances and low threat count rates. We discuss an anomaly detection algorithm that characterizes low count sources as threatening or non-threatening and operates well in the presence of high benign source variability. We discuss the algorithm parameters needed to reliably find sources both close to the detector and far away from it. These parameters include the cutoff frequencies of background tracking filters and the integration time of the spectrometer. This work is part of the development of the Standoff Radiation Imaging System (SORIS) as part of DNDOs Standoff Radiation Detection System Advanced Technology Demonstration (SORDS-ATD) program.

Eulerian Moment Equations for 2-D Stochastic Immiscible Flow

We solve statistical moment differential equations (MDEs) for immiscible flow in porous media in the limit of zero capillary pressure, with application to secondary oil recovery. Closure is achieved by Taylor expansion of the fractional flow function and a perturbation argument. Previous results in one dimension are extended to two dimensions. Comparison to Monte Carlo simulation (MCS) shows that the MDE approach gives a good approximation to total oil production. For such spatially integrated or averaged quantities MDEs may be substantially more efficient than MCS.

Femtosecond laser ablation multicollector ICPMS analysis of uranium isotopes in NIST glass

We utilized femtosecond laser ablation together with multi-collector inductively coupled plasma mass spectrometry to measure the uranium isotopic content of NIST 61x (x = 0, 2, 4, 6) glasses. The uranium content of these glasses is a linear two-component mixing between isotopically natural uranium and the isotopically depleted spike used in preparing the glasses. Laser ablation results match extremely well, generally within a few ppm, with solution analysis following sample dissolution and chemical separation. In addition to isotopic data, sample utilization efficiency measurements indicate that over 1% of ablated uranium atoms reach a mass spectrometer detector, making this technique extremely efficient. Laser sampling also allows for spatial analysis and our data indicate that rare uranium concentration inhomogeneities exist in NIST 616 glass.

Non-invasive material discrimination using spectral x-ray radiography

Current radiographic methods are limited in their ability to determine the presence of nuclear materials in containers or composite objects. A central problem is the inability to distinguish the attenuation pattern of high-density metals from those with a greater thickness of a less dense material. Here, we show that spectrally sensitive detectors can be used to discriminate plutonium from multiple layers of other materials using a single-view radiograph. An inverse algorithm with adaptive regularization is used. The algorithm can determine the presence of plutonium in simulated radiographs with a mass resolution per unit area of at least 0.07 g cm−2.

CDF Solutions of Buckley--Leverett Equation with Uncertain Parameters

The Buckley--Leverett (nonlinear advection) equation is often used to describe two-phase flow in porous media. We develop a new probabilistic method to quantify parametric uncertainty in the Buckley--Leverett model. Our approach is based on the concept of a fine-grained cumulative density function (CDF) and provides a full statistical description of the system states. Hence, it enables one to obtain not only average system response but also the probability of rare events, which is critical for risk assessment. We obtain a closed-form, semianalytical solution for the CDF of the state variable (fluid saturation) and test it against the results from Monte Carlo simulations.

Bayesian Radiation Source Localization

Abstract Locating illicit radiological sources using gamma-ray or neutron detection is a key challenge for both homeland security and nuclear nonproliferation. Localization methods using an array of detectors or a sequence of observations in time and space must provide rapid results while accounting for a dynamic attenuating environment. In the presence of significant attenuation and scatter, more extensive numerical transport calculations in place of the standard analytical approximations may be required to achieve accurate results. Numerical adjoints based on deterministic transport codes provide relatively efficient detector response calculations needed to determine the most likely location of a true source given a set of observed count rates. Probabilistic representations account for uncertainty in the source location resulting from uncertainties in detector responses and the potential for nonunique solutions. A Bayesian approach improves on previous likelihood methods for source localization by allowing the incorporation of all available information to help constrain solutions. We present an approach to localizing radiological sources that uses numerical adjoints and a Bayesian formulation and demonstrate the approach on two simple example scenarios. Results indicate accurate estimates of source locations. We briefly study the effect of neglecting the contribution of all scattered radiation in the adjoints, as analytical transport approximations do, for a case with moderately attenuating material between detectors and sources. The source location accuracy of the uncollided-only solutions appears to be significantly worse at the source strength considered here, suggesting that the higher physical fidelity that is provided by full numerical adjoint-based solutions may provide an advantage in operational settings.

A model of random sequences for de novo peptide sequencing

We present a model for the probability of random sequences appearing in product ion spectra obtained from tandem mass spectrometry experiments using collision-induced dissociation. We demonstrate the use of these probabilities for ranking candidate peptide sequences obtained using a de novo algorithm. Sequence candidates are obtained from a spectrum graph that is greatly reduced in size from those in previous graph-theoretical de novo approaches. Evidence of multiple instances of subsequences of each candidate, due to different fragment ion type series as well as isotopic peaks, is incorporated in a hierarchical scoring scheme. This approach is shown to be useful for confirming results from database search and as a first step towards a statistically rigorous de novo algorithm.

A Simulation Framework for Evaluating Detector Performance in Cargo Screening Applications

Deployed radiation portal monitor systems (RPMs) screen gamma-ray signatures of cargo at international border crossings with the goal of detecting illicit radiological materials. Estimating the detection sensitivity of these systems requires an in-depth understanding, and quantification, of RPM response to both benign and illicit sources. Benign sources of radioactivity include background, alterations of the background due to the presence of vehicles and cargo, as well as sources that frequently cause nuisance alarms. These nuisance sources, for example those consisting of naturally occurring radioactive materials (NORM) and medical isotopes, frequently limit system performance. Advanced detector technology promises to increase the capability of deployed systems to discriminate illicit from nuisance sources. Presented here is a framework developed to assess the performance of these passive detection technologies. Due to the difficulty in obtaining empirical data for emerging technologies, the foundation of this comparison framework lies on a simulated benign source population to create a comprehensive set of data representing cargo vehicles driving through the RPM. Quantification of performance stems from injecting simulated signatures from illicit sources and comparing probabilities of detection via case and population studies.