Marcus J. Neuer

Radiation detector signal processing using sampling kernels without bandlimiting constraints

For the development of digital signal processing systems for fast scintillation detectors we comprehensively study the modeling of nuclear signals, deconvolution of detector pulses and signal sampling. Applications for new scintillators with light decay times of a few nanoseconds demand suitable low power digital systems running at lowest possible sampling rates. We are interested in accurate sub-nanosecond timing and optimal energy resolution. The generalized non-bandlimiting sampling theorems allow filter structures with lower than Nyquist sampling rates for certain signals, where the classical sampling theorem fails. Recently it was shown that, by using a sampling rate greater or equal to the rate of innovation, it is possible to reconstruct certain restricted signals uniquely. The class of sampling kernels that can be used contains transfer functions with rational Fourier transforms. We introduce a physically realizable sampling scheme combined with a deconvolution timing filter algorithm for radiation detector signals. The novel architecture achieves sub-sampling rate timing accuracy together with optimal energy resolution and a high throughput for LaBr3:Ce scintillation detector system.

Linearization of Gamma Energy Spectra in Scintillator-Based Commercial Instruments

This paper presents a novel technique developed for linearizing the energy spectra of radiation detectors in commercial radioisotope identification devices. Based on few spectrum measurements with standard radio-nuclide sources, this method allows generation of individual nonlinear calibration functions at minimum expense in the routine instrument setup. Instead of fitting peak positions, the measured raw data are compared with simulated spectrum templates, and local gain factors providing the best correspondence are taken as reference points for the calibration function. This approach avoids the problem of fitting multiple peaks with intensity ratios influenced by absorbing layers and assures an accuracy of 1% in the energy range of 30 keV to 3 MeV.

A technique for estimating detection limits of radio-nuclide identifying detectors by means of computer simulations

A simulation technique has been developed to study the performance of nuclide identifying gamma detector units operating under a variety of different conditions. The studies are related to nuclide identification based on a template matching algorithm which has been well established, in particular, for the analysis of low statistics measurements. Results are presented for hand-held devices equipped with NaI(Tl) and LaBr3(Ce3+) scintillation detectors, respectively

Evolutionary ensembles that learn spectroscopic characteristics of scintillation and CZT detectors

A method is described to automatically generate spectrum reference data for radioisotope identification devices respecting a detectors physical individuality. It extracts the peak shape and non-proportionality characteristics of scintillation and CZT detectors. The representation of these quantities is done with evolutionary ensembles, groups of N-dimensional autonomous points, which are propagated within a constrained space. Each ensemble member is used as parametrical input for describing peak shape and position within a simulation framework based on Geant4. Each subsequent generation of the ensemble iteratively converges the simulation result towards an optimised match with the measurement. Examples for the scintillator show that the shape convergence is straightforward due to the gaussianity of the peak, while the correction of the non-proportionality is within the quantity of up to 10%. Contrarily, our CZT example yielded nearly no non-proportionality along the energy scale, but required a complex, multi-parametrical shape definition with learning curves for kurtosis, skewness and resolution to establish an adequately peak reproduction. A metric is presented to calculate the distance between the experimental data and the calculated result. The described system is suited to establish a production line with a fully automatised acquisition of spectral characteristics to support the deployment of detector individual reference data for nuclide identification instrumentation.

A Cognitive Filter to Automatically Determine Scintillation Detector Materials and to Control Their Spectroscopic Resolution During Temperature Changes

The shape of the nuclear signal from scintillators contains information about the detection material from which it originated and its current temperature. A digital filter for this type of signals is presented that automatically extracts both of these pieces of information from the shape of the signals and controls the temperature dependency of the resolution by continuous adaption to this shape. Hereby, nuclear signals are a-priori modelled by an all-pole filter. The concept combines a deconvolution approach based on this model with a least-mean-squares iteration. Beneath the iteration, there is a continuous assessment of the signal shape where the mean-squared distance between the assumed shape and measured shape acts as control parameter for the resolution. From the information stored inside the dynamic filter parameters, the material characteristics are retrieved to identify the detector material. An implementation into a multi-channel-analyser is shown and the technique is verified under real-world environmental conditions.

Surveillance of nuclear threats using multiple, autonomous detection units

A detection systems is presented that involves multiple autonomous detectors, which are merged to a net like structure. Each unit is equipped with a stabilized and linearized multichannel analyzer for the measurement of gamma spectra and optionally a He3 neutron detector. The flexible hard- and software architecture allows the simple integration of a variable number of detectors, even with different volumes. With this technique, spectroscopical screening of large areas, e.g. airports or comparably sensitive objects, can be established by an individual combination of these detectors. The general outline of the nuclide identification algorithm is presented. In the context of medical isotopes, the importance of correct reference data that incorporates the scattering effects of human bodies is clarified. A special focus is drawn to the localization of nuclear sources. The model, corresponding simulations and its applications are depicted. Different tests and verification measures are described that refer to the nuclide identification and the source localization as well as to the spectral quality of the detectors. A finished real application is presented that combines the tested algorithms and has proven its reliability on different occasions.

Underwater nuclide identification strategy using a multi-agent system with a dedicated scattering and attenuation agent

Scattering and attenuation have significant impact on the spectrum. For submersible nuclide identification devices, it is mandatory to still provide high quality identification results, even if the instrument is in the water. A technique is shown, to treat the effects of water scattering. This is done by combining the so-called dynamic derivative convolution method and a maximum-likelihood approach. Several low energy peaks can be reconstructed. Tests were performed with a commercial radio-isotope identification device, which was submerged in a defined testing pool.

Dynamic derivative convolution algorithm for prompt gamma neutron activation spectra

A technique is presented to algorithmically evaluate prompt gamma neutron activation spectra, which were produced through excitation of specific material samples. The excitation is done with a neutron generator that provides a switchable, artificial form of neutron radiation. To evaluate the spectra, a extension to prior peak based analysis methods is proposed that dynamically incorporates the detector resolution over the full energy range. Based on this technique a series of measured response spectra are analyzed and the material composition or the samples is identified. Materials of cement and coal mining industry are considered. Limits of the detection limits and confidence thresholds are presented.

Model-based, analytical maximum-likelihood deconvolution for CZT detectors

An analytical response function is presented that is based on a perturbed Pearson-IV model for the peak shape and additional models for the first four moments of the distribution, mean, variance, skewness and kurtosis. The parametrisation of these moment models is acquired by using an evolutionary ensemble algorithm, delivering corresponding energy dependent functions for each of the moments. Using the relationship between the moments and the actual peak shape, this analytical equation is introduced into the Maximum-Likelihood inversion, delivering a formula for deconvolving spectra. Through the parametrisation, the technique can be tailored to any CZT detector. Without having to store a large response matrix, the analytical deconvolution can be easily deployed also on smaller devices, with less computational performance. The technique is validated on a Raspberry Pi connected to a muSPEC500 device from Ritec, featuring a 300mm3 CZT crystal. Example measurements for cesium 137Cs, cobalt 60Co, europium 152Eu and uranium 238U are deconvolved and show a significant capability of resolving peaks.

A cognitive filter to stabilize peak positions and widths of a scintillation detector and to determine its material

A digital filter is presented that adapts automatically to the shape of a nuclear signal to stabilise against temperature induced peak shifting. The filter is called cognitive as it extracts the information about the exponential decays and determines the scintillation material intrinsically. The latter is done with a pole deconvolution approach.Based on the knowledge of the decays, the peak position in the pulse-height spectrum is stabilised due to the relationship between exponential decay and temperature. To achieve this, the peak positions are experimentally acquired within a climate chamber measurement and later fed into a learning artificial neural network. This network has additionally access to the decay times, the temperature and the speed and direction of the temperature. The filter is called every two minutes, automatically collecting a series of 500 suited signals and performing an update of the stabilisation. In tests, the temperature induced peak shifting was corrected within a 0.2% boundary. An additional output result of the filter is the material type of the scintillator. For the most materials, temperature curves are known and the assessment of the exponential decay is shown to lead straightforwardly to a determination of the material. Also the hot plugging of two materials is possible, yielding a recovery time of approximately four minutes for the system to adapt to the new material.