<p> Thermal neutron capture studies have been made of the odd-odd nuclei, 20F, 24Na, 28Al, 32P, 36Cl and 40K. The gamma ray spectra have been studied using a Ge(Li) pair spectrometer and revised decay schemes are presented for these nuclei. The Q-values and gamma ray multiplicities have been obtained for the reactions studied. The average partial widths for primary Ml and El transitions have been calculated for the six nuclei and compared with the theoretical estimates. The features of the capture gamma ray spectra are discussed and it is observed that for the six nuclei studied, the total intensity in the spectrum above the energy of 0.3 times the Q-value is nearly the same (~1.4 photons/capture).</p> / Thesis / Doctor of Philosophy (PhD)

Techniques of beta- and gamma-ray scintillation spectroscopy have been applied to a study of the decay radiations from 92.6-min La142. Several new gamma-ray transitions have been discovered and a gamma-gamma coincidence matrix has been established. Nine beta groups have been identified and evidence is presented substantiating the assignment of a first-forbidden unique character to the ground-state beta transition. Angular correlation studies have been performed on some of the gamma cascades. A decay-scheme based on the experimental results is discussed and some spin assignments are made. Some features of the decay modes are interpreted within the context of contemporary ideas about nuclear structure. / Thesis / Doctor of Philosophy (PhD)

<p> Contemporary approaches to data analysis suffer from being both time-consuming and subjective; however, the application of numerical techniques to the automated (non-interactive) analysis of gamma ray spectra often leads to considerably improved performance. The foundations and limitations of such techniques lie in the applicability of certain mathematical operations such as deconvolution, and the careful study of stochastic models. The use of digital filters as a method of enhancing detector response has been applied to a triple-coincidence counting arrangement, after modelling undesired physical effects. An objective background estimation method has been described based on the statistical nature of nuclear measurements. Finally, the application of such techniques is demonstrated with a package of FORTRAN programs designed to be used in a variety of situations with minimal modifications. </p> / Thesis / Master of Science (MSc)

The purpose of this project is to simulate the detection of γ-ray spectra emitted by radon isotopes and their daughters. This is done as a contribution to the development of radiation detectors to be used in a research project investigating the possibility of using increased amounts of the radioactive gas radon as an earthquake precursor. Before the onset of an earthquake, microcracks are formed in the surrounding stone structures due to stress, releasing greater than usual amounts of radon gas contained within the rock pores. A way of predicting an upcoming earthquake would then be to place radiation detectors in areas with high seismicity in order to measure possible changes. This could be done in soil, groundwater (via springs, wells, and boreholes), or air. In this project, we aim to understand how measurements in groundwater would differ from ones in air, and how to best make use of the spectra as seen in water. This was done by simulating a scenario in which a scintillator detector, made of cesium iodide, is placed in each media and then assessing the resulting γ-ray spectra.

No description available.

The quality of the spectral data collected by radiological survey systems depends on many factors including the survey environment, configuration of the system and its detectors, and the radionuclides in question. Algorithms in the field of machine learning have the potential to classify data that would be difficult and time-intensive for a human to analyze. Depleted and natural uranium spectra are of particular interest due to known contamination at domestic sites and world-wide. Several machine learning classifiers were developed with data collected from laboratory experiments. This thesis demonstrates the potential of machine learning algorithms to discriminate gamma-ray emitting sources using sparse, or low-count statistic, data. Effectiveness has been demonstrated for discriminating chemical forms of uranium, mixtures with differing uranium isotope distributions, and predicting source masses given certain detector geometries and a known target distribution. All activity has been supported by the U.S. Army Engineering Research and Development Center (ERDC).

A quantitative analysis was performed on a sample of Fast-Rise Exponential-Decay gamma-ray bursts using a continuous fitting function. The data were obtained from the Large Area detector catalogue of the Burst and Transient Source Experiment, which flew from 1991-2000 onboard the Compton Gamma-Ray Observatory. The purpose here is to tabulate from the fits quantities associated with gamma-ray bursts, emphasising peak intensity, duration, and characteristic rates of rising and falling. V/Vmax and duration analyses show that the sample of bursts is representative of the larger population of gamma-ray bursts. A modified asymmetric double sigmoidal was found to fit the background subtracted peaks for the majority of bursts. From the regressions the amplitude, duration and rising and falling characteristic times are defined for each burst, as well as a value describing the asymmetry of the peak. These values are compared with each other and with catalogued values of duration and V/Vmax.

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No description available.

No description available.