Velocity-sensitised Magnetic Resonance Imaging of foams

Although flowing foams are used in a variety of technologies, foam rheology is still incompletely understood. In this paper we demonstrate the use of a velocity-sensitised magnetic resonance imaging (MRI) sequence for the study of flowing foam. We employ a constant-time (pure phase encode) imaging technique, SPRITE, which is immune to geometrical distortions caused by the foam-induced magnetic field inhomogeneity. The sample magnetisation is prepared before the SPRITE imaging with the Cotts 13-interval motion-sensitisation sequence, which is also insensitive to the effects of the foam heterogeneity. We measure the development of a power-law velocity profile in the foam downstream of a Venturi constriction (in which the cross-section of the tube decreases by 89% in area) in a vertical, cylindrical pipe

Spatially-resolved magnetic resonance study of the dissolution interface between soaps and water

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Parameter estimation with Bayesian estimation applied to multiple species in the presence of biases and correlations

The original formulation of Bayesian estimation applied to multiple species (BEAMS) showed how to use a data set contaminated by points of multiple underlying types to perform unbiased parameter estimation. An example is cosmological parameter estimation from a photometric supernova sample contaminated by unknown Type Ibc and II supernovae. Where other methods require data cuts to increase purity, BEAMS uses all of the data points in conjunction with their probabilities of being each type. Here we extend the BEAMS formalism to allow for correlations between the data and the type probabilities of the objects as can occur in realistic cases. We show with simple simulations that this extension can be crucial, providing a 50 per cent reduction in parameter estimation variance when such correlations do exist. We then go on to perform tests to quantify the importance of the type probabilities, one of which illustrates the effect of biasing the probabilities in various ways. Finally, a general presentation of the selection bias problem is given, and discussed in the context of future photometric supernova surveys and BEAMS, which lead to specific recommendations for future supernova survey

BEAMS: separating the wheat from the chaff in supernova analysis

We introduce Bayesian Estimation Applied to Multiple Species (BEAMS), an algorithm designed to deal with parameter estimation when using contaminated data. We present the algorithm and demonstrate how it works with the help of a Gaussian simulation. We then apply it to supernova data from the Sloan Digital Sky Survey (SDSS), showing how the resulting confidence contours of the cosmological parameters shrink significantly.Comment: 23 pages, 9 figures. Chapter 4 in "Astrostatistical Challenges for the New Astronomy" (Joseph M. Hilbe, ed., Springer, New York, forthcoming in 2012), the inaugural volume for the Springer Series in Astrostatistic

Using phase interference to characterize dynamic properties—a review of constant gradient, portable magnetic resonance methods

Spatially resolved motion-sensitized magnetic resonance (MR) is a powerful tool for studying the dynamic properties of materials. Traditional methods involve using large, expensive equipment to create images of sample displacement by measuring the spatially resolved MR signal response to time-varying magnetic field gradients. In these systems, both the sample and the stress applicator are typically positioned inside a magnet bore. Portable MR instruments with constant gradients are more accessible, with fewer limitations on sample size, and they can be used in industrial settings to study samples under deformation or flow. We propose a view in which the well-controlled sensitive region of a magnet array acts as an integrator, with the velocity distribution leading to phase interference in the detected signal, which encodes information on the sample’s dynamic properties. For example, in laminar flows of Newtonian and non-Newtonian fluids, the velocity distribution can be determined analytically and used to extract the fluid’s dynamic properties from the MR signal magnitude and/or phase. This review covers general procedures, practical considerations, and examples of applications in dynamic mechanical analysis and fluid rheology (viscoelastic deformation, laminar pipe flows, and Couette flows). Given that these techniques are relatively uncommon in the broader magnetic resonance community, this review is intended for both advanced NMR users and a more general physics/engineering audience interested in rheological applications of NMR

Statistical classification techniques for photometric supernova typing

Future photometric supernova surveys will produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effective classification methods based on light curves alone. Here we introduce boosting and kernel density estimation techniques which have minimal astrophysical input, and compare their performance on 20 000 simulated Dark Energy Survey light curves. We demonstrate that these methods perform very well provided a representative sample of the full population is used for training. Interestingly, we find that they do not require the redshift of the host galaxy or candidate supernova. However, training on the types of spectroscopic subsamples currently produced by supernova surveys leads to poor performance due to the resulting bias in training, and we recommend that special attention be given to the creation of representative training samples. We show that given a typical non-representative training sample, S, one can expect to pull out a representative subsample of about 10 per cent of the size of S, which is large enough to outperform the methods trained on all of

Statistical Classification Techniques for Photometric Supernova Typing

Future photometric supernova surveys will produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effective classification methods based on lightcurves alone. Here we introduce boosting and kernel density estimation techniques which have minimal astrophysical input, and compare their performance on 20,000 simulated Dark Energy Survey lightcurves. We demonstrate that these methods are comparable to the best template fitting methods currently used, and in particular do not require the redshift of the host galaxy or candidate. However both methods require a training sample that is representative of the full population, so typical spectroscopic supernova subsamples will lead to poor performance. To enable the full potential of such blind methods, we recommend that representative training samples should be used and so specific attention should be given to their creation in the design phase of future photometric surveys.Comment: 19 pages, 41 figures. No changes. Additional material and summary video available at http://cosmoaims.wordpress.com/2010/09/30/boosting-for-supernova-classification