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Williams, Brian J

Publications and source records attributed to Williams, Brian J.

DUST IN A TYPE Ia SUPERNOVA PROGENITOR: SPITZER SPECTROSCOPY OF KEPLER'S SUPERNOVA REMNANT

Characterization of the relatively poorly understood progenitor systems of Type Ia supernovae is of great importance in astrophysics, particularly given the important cosmological role that these supernovae play. Kepler's supernova remnant, the result of a Type Ia supernova, shows evidence for an interaction with a dense circumstellar medium (CSM), suggesting a single-degenerate progenitor system. We present 7.5-38 {mu}m infrared (IR) spectra of the remnant, obtained with the Spitzer Space Telescope, dominated by emission from warm dust. Broad spectral features at 10 and 18 {mu}m, consistent with various silicate particles, are seen throughout. These silicates were likely formed in the stellar outflow from the progenitor system during the asymptotic giant branch stage of evolution, and imply an oxygen-rich chemistry. In addition to silicate dust, a second component, possibly carbonaceous dust, is necessary to account for the short-wavelength Infrared Spectrograph and Infrared Array Camera data. This could imply a mixed chemistry in the atmosphere of the progenitor system. However, non-spherical metallic iron inclusions within silicate grains provide an alternative solution. Models of collisionally heated dust emission from fast shocks (>1000 km s{sup -1}) propagating into the CSM can reproduce the majority of the emission associated with non-radiative filaments, where dust temperatures are {approx}80-100 K, but fail to account for the highest temperatures detected, in excess of 150 K. We find that slower shocks (a few hundred km s{sup -1}) into moderate density material (n{sub 0} {approx} 50-250 cm{sup -3}) are the only viable source of heating for this hottest dust. We confirm the finding of an overall density gradient, with densities in the north being an order of magnitude greater than those in the south.

79 ASTRONOMY AND ASTROPHYSICS↗

Sequential Importance Sampling for Rare Event Estimation with Computer Experiments

Importance sampling often drastically improves the variance of percentile and quantile estimators of rare events. We propose a sequential strategy for iterative refinement of importance distributions for sampling uncertain inputs to a computer model to estimate quantiles of model output or the probability that the model output exceeds a fixed or random threshold. A framework is introduced for updating a model surrogate to maximize its predictive capability for rare event estimation with sequential importance sampling. Examples of the proposed methodology involving materials strength and nuclear reactor applications will be presented. The conclusions are: (1) Importance sampling improves UQ of percentile and quantile estimates relative to brute force approach; (2) Benefits of importance sampling increase as percentiles become more extreme; (3) Iterative refinement improves importance distributions in relatively few iterations; (4) Surrogates are necessary for slow running codes; (5) Sequential design improves surrogate quality in region of parameter space indicated by importance distributions; and (6) Importance distributions and VRFs stabilize quickly, while quantile estimates may converge slowly.

97 MATHEMATICS AND COMPUTING↗