A Spectral CT Method to Directly Estimate Basis Material Maps From Experimental Photon-Counting Data

The proposed spectral CT method solves the constrained one-step spectral CT reconstruction (cOSSCIR) optimization problem to estimate basis material maps while modeling the nonlinear X-ray detection process and enforcing convex constraints on the basis map images. In order to apply the optimization-based reconstruction approach to experimental data, the presented method empirically estimates the effective energy-window spectra using a calibration procedure. The amplitudes of the estimated spectra were further optimized as part of the reconstruction process to reduce ring artifacts. A validation approach was developed to select constraint parameters. The proposed spectral CT method was evaluated through simulations and experiments with a photon-counting detector. Basis material map images were successfully reconstructed using the presented empirical spectral modeling and cOSSCIR optimization approach. In simulations, the cOSSCIR approach accurately reconstructed the basis map images

New limb-darkening coefficients and synthetic photometry for model-atmosphere grids at Galactic, LMC, and SMC abundances

New grids of Atlas9 models have been calculated using revised convection parameters and updated opacity-distribution functions, for chemical compositions intended to be representative of solar, [M/H] = +0.3, +0.5, Large Magellanic Cloud (LMC), and Small Magellanic Cloud (SMC) abundances. The grids cover T(eff) = 3.5-50kK, from log(g) = 5.0 to the effective Eddington limit. Limb-darkening coefficients and synthetic photometry are presented in the UBVRIJHKLM, uvby, ugriz, WFCAM, Hipparcos/Tycho, and Kepler passbands for these models, and for Castelli's comparable `new-ODF' grids. Flux distributions are given for the new models. The sensitivity of limb-darkening coefficients to the adopted physics is illustrated

The Calibration and Data Products of the Galaxy Evolution Explorer

We describe the calibration status and data products pertaining to the GR2 and GR3 data releases of the Galaxy Evolution Explorer (GALEX). These releases have identical pipeline calibrations that are significantly improved over the GR1 data release. GALEX continues to survey the sky in the Far Ultraviolet (FUV, ~154 nm) and Near Ultraviolet (NUV, ~232 nm) bands, providing simultaneous imaging with a pair of photon counting, microchannel plate, delay line readout detectors. These 1.25 degree field-of-view detectors are well-suited to ultraviolet observations because of their excellent red rejection and negligible background. A dithered mode of observing and photon list output pose complex requirements on the data processing pipeline, entangling detector calibrations and aspect reconstruction algorithms. Recent improvements have achieved photometric repeatability of 0.05 and 0.03 mAB in the FUV and NUV, respectively. We have detected a long term drift of order 1% FUV and 6% NUV over the mission. Astrometric precision is of order 0.5" RMS in both bands. In this paper we provide the GALEX user with a broad overview of the calibration issues likely to be confronted in the current release. Improvements are likely as the GALEX mission continues into an extended phase with a healthy instrument, no consumables, and increased opportunities for guest investigations.Comment: Accepted to the ApJS (a special GALEX issue

Bridging the capability gap in environmental gamma-ray spectrometry

Environmental gamma-ray spectroscopy provides a powerful tool that can be used in environmental monitoring given that it offers a compromise between measurement time and accuracy allowing for large areas to be surveyed quickly and relatively inexpensively. Depending on monitoring objectives, spectral information can then be analysed in real-time or post survey to characterise contamination and identify potential anomalies. Smaller volume detectors are of particular worth to environmental surveys as they can be operated in the most demanding environments. However, difficulties are encountered in the selection of an appropriate detector that is robust enough for environmental surveying yet still provides a high quality signal. Furthermore, shortcomings remain with methods employed for robust spectral processing since a number of complexities need to be overcome including: the non-linearity in detector response with source burial depth, large counting uncertainties, accounting for the heterogeneity in the natural background and unreliable methods for detector calibration. This thesis aimed to investigate the application of machine learning algorithms to environmental gamma-ray spectroscopy data to identify changes in spectral shape within large Monte Carlo calibration libraries to estimate source characteristics for unseen field results. Additionally, a 71 × 71 mm lanthanum bromide detector was tested alongside a conventional 71 × 71 mm sodium iodide to assess whether its higher energy efficiency and resolution could make it more reliable in handheld surveys. The research presented in this thesis demonstrates that machine learning algorithms could be successfully applied to noisy spectra to produce valuable source estimates. Of note, were the novel characterisation estimates made on borehole and handheld detector measurements taken from land historically contaminated with 226Ra. Through a novel combination of noise suppression and neural networks the burial depth, activity and source extent of contamination was estimated and mapped. Furthermore, it was demonstrated that Machine Learning techniques could be operated in real-time to identify hazardous 226Ra containing hot particles with much greater confidence than current deterministic approaches such as the gross counting algorithm. It was concluded that remediation of 226Ra contaminated legacy sites could be greatly improved using the methods described in this thesis. Finally, Neural Networks were also applied to estimate the activity distribution of 137Cs, derived from the nuclear industry, in an estuarine environment. Findings demonstrated the method to be theoretically sound, but practically inconclusive, given that much of the contamination at the site was buried beyond the detection limits of the method. It was generally concluded that the noise posed by intrinsic counts in the 71 × 71 mm lanthanum bromide was too substantial to make any significant improvements over a comparable sodium iodide in contamination characterisation using 1 second counts