[en] The development of high-speed, high-performance gamma-ray spectroscopy algorithms is critical to the success of many automated threat detection systems. In response to this need a proliferation of such algorithms has taken place. With this proliferation comes the necessary and non-trivial task of validation. There is (and always will be) insufficient experimental data to determine performance of spectroscopy algorithms over the relevant factor space at any reasonable precision. In the case of gamma-ray spectroscopy, there are hundreds of radioisotopes of interest, which may come in arbitrary admixtures, there are many materials of unknown quantity, which may be found in the intervening space between the source and the detection system, and there are also irregular variations in the detector systems themselves. All of these factors and more should be explored to determine algorithm/system performance. This paper describes a statistical framework for the performance estimation and comparison of gamma-ray spectroscopy algorithms. The framework relies heavily on data of increasing levels of artificiality to sufficiently cover the factor space. At each level rigorous statistical methods are employed to validate performance estimates.

[en] Today there is a tremendous amount of interest in systems that can detect radiological or nuclear threats. Many of these systems operate in extremely high throughput situations where delays caused by false alarms can have a significant negative impact. Thus, calculating the tradeoff between detection rates and false alarm rates is critical for their successful operation. Receiver operating characteristic (ROC) curves have long been used to depict this tradeoff. The methodology was first developed in the field of signal detection. In recent years it has been used increasingly in machine learning and data mining applications. It follows that this methodology could be applied to radiological/nuclear threat detection systems. However many of these systems do not fit into the classic principles of statistical detection theory because they tend to lack tractable likelihood functions and have many parameters, which, in general, do not have a one-to-one correspondence with the detection classes. This work proposes a strategy to overcome these problems by empirically finding parameter values that maximize the probability of detection for a selected number of probabilities of false alarm. To find these parameter values a statistical global optimization technique that seeks to estimate portions of a ROC curve is proposed. The optimization combines elements of simulated annealing with elements of genetic algorithms. Genetic algorithms were chosen because they can reduce the risk of getting stuck in local minima. However classic genetic algorithms operate on arrays of Booleans values or bit strings, so simulated annealing is employed to perform mutation in the genetic algorithm. The presented initial results were generated using an isotope identification algorithm developed at Johns Hopkins University Applied Physics Laboratory. The algorithm has 12 parameters: 4 real-valued and 8 Boolean. A simulated dataset was used for the optimization study; the 'threat' set of spectra contained 540 SNM and industrial signatures, and the 'benign' set of spectra contained 240 NORM and medical signatures. As compared to a random parameter search, the statistical optimization was able to able to find parameters that yield significantly higher probabilities of detection for all probabilities of false alarm from 0 to 0.1 (and equal to for probabilities of false alarm greater than 0.1), in a relatively small number of iterations. The number of iterations used, 1000, is also many fewer than would be required for a reasonable systematic search of the parameter space.

[en] An experimental study of the 16O(e, e'K+)16N_#Lambda# reaction has been performed at Jefferson Lab. A thin film of falling water was used as a target. This permitted a simultaneous measurement of the p(e, e'K+)Λ,Σ₀ exclusive reactions and a precise calibration of the energy scale. A ground-state binding energy of 13.76 ± 0.16 MeV was obtained for 16N_#Lambda# with better precision than previous measurements on the mirror hypernucleus 16O_#Lambda#. Precise energies have been determined for peaks arising from a Lambda in s and p orbits coupled to the p1/2 and p3/2 hole states of the 15N core nucleus.

[en] The reaction ²H(e,e(prime) p)n has been studied with full kinematic coverage for photon virtuality 1.75 < 5.5 ∼ GeV². Comparisons of experimental data with theory indicate that for very low values of neutron recoil momentum (p_n < 100 MeV/c) the neutron is primarily a spectator and the reaction can be described by the plane-wave impulse approximation. For 100 < 750 MeV/c proton-neutron rescattering dominates the cross section, while Δ production followed by the NΔ → NN transition is the primary contribution at higher momenta