The MPE/UCB far-infrared imaging Fabry-Perot interferometer (FIFI)

FIFI is an imaging spectrometer with two or three Fabry-Perot interferometers (FPI) in series for airborne astronomical observations in the far-infrared range (=40...200m). It employs 5×5 arrays of photoconducting detectors and offers spectral resolutions as small as 2km/s. Resolution and bandwidth can be set over a wide range to match a variety of astronomical sources. Cryogenic optics minimizes thermal background radiation and provides for in-flight step tunable spatial resolution. At 158 m wavelength the background-limited NEP is 3 × 10^-15W/Hz at 40 km/s resolution and with two FPI's; with three FPI's the expected NEP is 10^-15WHz at 5 km/s resolution.The frequency-chopping mode of the high-resolution Fabry-Perot allows for line detection in extended objects. Absolute internal flux calibration ensures adequate flat fielding of the array elements.     

An improved error bound for a finite element approximation of a model for phase separation of a multi-component alloy

Using the approach of Rulla (1996 SIAM J. Numer. Anal. 33, 68-87)for analysing the time discretization error and assuming moreregularity on the initial data, we improve on the error boundderived by Barrett and Blowey (1996 IMA J. Numer. Anal. 16,257-287) for a fully practical piecewise linear finite elementapproximation with a backward Euler time discretization of amodel for phase separation of a multi-component alloy.     

A cryogenic underground observatory for rare events: CUORE, an update

CUORE is a proposed tightly packed array of 1000 TeO₂ bolometers, each being a cube 5 cm on a side with a mass of 750 g. The array consists of 25 vertical towers, arranged in a square of 5 towers by 5 towers, each containing ten layers of four crystals. The design of the detector is optimized for ultralow-background searches for neutrinoless double beta decay of ¹³⁰Te (33.8% abundance), cold dark matter, solar axions, and rare nuclear decays. A preliminary experiment involving 20 crystals of various sizes (MIBETA) has been completed, and a single CUORE tower is being constructed as a smaller scale experiment called CUORICINO. The expected performance and sensitivity, based on Monte Carlo simulations and extrapolations of present results, are reported.     

Bayesian Modeling of NMR Data: Quantifying Longitudinal Relaxation in Vivo,and in Vitro with a Tissue-Water-Relaxation Mimic (Crosslinked Bovine Serum Albumin)

Recently, a number of magnetic resonance imaging protocols have been reported that seek to exploit the effect of dissolved oxygen (O₂, paramagnetic) on the longitudinal ¹H relaxation of tissue water, thus providing image contrast related to tissue oxygen content. However, tissue water relaxation is dependent on a number of mechanisms and this raises the issue of how best to model the relaxation data. This problem, the model selection problem, occurs in many branches of science and is optimally addressed by Bayesian probability theory. High signal-to-noise, densely sampled, longitudinal ¹H relaxation data were acquired from rat brain in vivo and from a cross-linked bovine serum albumin (xBSA) phantom, a sample that recapitulates the relaxation characteristics of tissue water in vivo. Bayesian-based model selection was applied to a cohort of five competing relaxation models: (1) monoexponential, (2) stretched-exponential, (3) biexponential, (4) Gaussian (normal) R ₁-distribution, and (5) gamma R ₁-distribution. Bayesian joint analysis of multiple replicate datasets revealed that water relaxation of both the xBSA phantom and in vivo rat brain was best described by a biexponential model, while xBSA relaxation datasets truncated to remove evidence of the fast relaxation component were best modeled as a stretched exponential. In all cases, estimated model parameters were compared to the commonly used monoexponential model. Reducing the sampling density of the relaxation data and adding Gaussian-distributed noise served to simulate cases in which the data are acquisition-time or signal-to-noise restricted, respectively. As expected, reducing either the number of data points or the signal-to-noise increases the uncertainty in estimated parameters and, ultimately, reduces support for more complex relaxation models.