Efficient Two-dimensional Subrecoil Raman Cooling of Atoms in a Tripod Configuration

We present an efficient method for subrecoil cooling of neutral atoms by applying Raman cooling in 2D to a four-level tripod-system. The atoms can be cooled simultaneously in two directions using only three laser beams. We describe the cooling process with a simple model showing that the momentum distribution can be rapidly narrowed to velocity spread down to $0.1v_\text{rec}$, corresponding to effective temperature equal to $0.01T_\text{rec}$. This method opens new possibilities for cooling of neutral atoms.Comment: 6 pages, 3 figure

Interview with Fred Brooks on “Building Effective Large-Scale Requirements”

Not availabl

Spatial variability - So what?

Since the landmark papers of Conway and Abrahamson many studies have tried to quantify spatial variability. Many different methods have been used and the studies covered a variety of scales. Accordingly, some results appear contradictory, suggesting that the degree of spatial variation varies widely. This is not surprising, and is partly due to the methodology used and of course, due to varying natural conditions. Spatial variability is doubtless an inherent property of the snowpack. One important result seems to be that the layering is less variable than, for example, the stability of small column tests. Whereas it is often perceived that the results of the studies were not conclusive, it seems clear that they completely changed our view of spatial variability. We realized the importance of scale issues. For example, the variation will strongly depend on the measurement scale – the so-called support – of the method (SnowMicroPen vs. compression test vs. rutschblock test). Geostatistical analysis has been introduced and used to derive appropriate input data for numerical models. Model results suggest that spatial variation of strength properties have a substantial knockdown effect on slope stability and that the effect increases with increasing spatial correlation. The focus on scale has also revealed that spatial variations can promote instability or inhibit it. With the awareness of scale we can now address the causes of spatial variability. Many processes such as radiation and wind act at several scales. The most challenging process is probably wind that might hinder prediction of variability at the slope scale. However, at the regional scale, already today, many avalanche forecasting services try to address differences in respect to slope aspect. We will review the present state of knowledge, discuss consequences for avalanche forecasting and snow stability evaluation, and recommend future research directions

Spatial variability - so what?

Since the landmark papers of Conway and Abrahamson many studies have tried to quantify spatial variability. Many different methods have been used and the studies covered a variety of scales. Accordingly, some results appear contradictory, suggesting that the degree of spatial variation varies widely. This is not surprising, and is partly due to the methodology used and of course, due to varying natural conditions. Spatial variability is doubtless an inherent property of the snowpack. One important result seems to be that the layering is less variable than, for example, the stability of small column tests. Whereas it is often perceived that the results of the studies were not conclusive, it seems clear that they completely changed our view of spatial variability. We realized the importance of scale issues. For example, the variation will strongly depend on the measurement scale – the so-called support – of the method (SnowMicroPen vs. compression test vs. rutschblock test). Geostatistical analysis has been intro duced and used to derive appropriate input data for numerical models. Model results suggest that spatial variation of strength properties have a substantial knockdown effect on slope stability and that the effect increases with increasing spatial correlation. The focus on scale has also revealed that spatial variations can promote instability or inhibit it. With the awareness of scale we can now address the causes of spatial variability. Many processes such as radiation and wind act at several scales. The most challenging process is probably wind that might hinder prediction of variability at the slope scale. However, at the regional scale, already today, many avalanche forecasting services try to address differences in respect to slope aspect. We will review the present state of knowledge, discuss consequences for avalanche forecasting and snow stability evaluation, and recommend future research directions

Temporal changes in the spatial variability of shear strength and stability.

Avalanche forecasting involves the prediction of spatial and temporal variability of the snowpack. To predict avalanches with more accuracy it is important to determine whether the snowpack is becoming more spatially variable or more spatially uniform. Greater variability increases uncertainty in extrapolation and prediction. Our results offer a look at the evolution of the spatial variability of shear strength and stability of a buried surface hoar layer in southwestern Montana, USA, from shortly after burial until it was no longer the weakest layer in the snowpack. We selected the study site for its 27- degree planar slope, uniform ground cover, and wind-sheltered location. This simplified the comparison of the plots by minimizing initial spatial differences so we could focus on temporal change. Within the site, we sampled four 14 m x 14 m arrays of more than 70 shear frame tests in a layout optimized for spatial analysis. Over a three-week period, the sampling of the four adjacent arrays showed temporal change. The variability of the shear strength of this layer initially decreased then became increasingly variable through time. This suggests that extrapolating test results to other locations becomes increasingly unreliable as layers age, a result that matches practical experience. The data also provide indications that shear strength has a correlation length, the distance at which test results are related, of just a few meters. This short correlation length demonstrates quantitatively why stability tests that are relatively close together can be quite different

Temporal changes in the spatial variability of shear strength and stability

Avalanche forecasting involves the prediction of spatial and temporal variability of the snowpack. To predict avalanches with more accuracy it is important to determine whether the snowpack is becoming more spatially variable or more spatially uniform. Greater variability increases uncertainty in extrapolation and prediction. Our results offer a look at the evolution of the spatial variability of shear strength and stability of a buried surface hoar layer in southwestern Montana, USA, from shortly after burial until it was no longer the weakest layer in the snowpack. We selected the study site for its 27- degree planar slope, uniform ground cover, and wind-sheltered location. This simplified the comparison of the plots by minimizing initial spatial differences so we could focus on temporal change. Within the site, we sampled four 14 m x 14 m arrays of more than 70 shear frame tests in a layout optimized for spatial analysis. Over a three-week period, the sampling of the four adjacent arrays showed temporal change. The variability of the shear strength of this layer initially decreased then became increasingly variable through time. This suggests that extrapolating test results to other locations becomes increasingly unreliable as layers age, a result that matches practical experience. The data also provide indications that shear strength has a correlation length, the distance at which test results are related, of just a few meters. This short correlation length demonstrates quantitatively why stability tests that are relatively close together can be quite different

Collective molecule formation in a degenerate Fermi gas via a Feshbach resonance

We model collisionless collective conversion of a degenerate Fermi gas into bosonic molecules via a Feshbach resonance, treating the bosonic molecules as a classical field and seeding the pairing amplitudes with random phases. A dynamical instability of the Fermi sea against association into molecules initiates the conversion. The model qualitatively reproduces several experimental observations {[Regal et al., Nature {\bf 424}, 47 (2003)]}. We predict that the initial temperature of the Fermi gas sets the limit for the efficiency of atom-molecule conversion.Comment: 4 pages, 3 figures, 10+ references, accepted to PR

Approximate Quantum Fourier Transform and Decoherence

We discuss the advantages of using the approximate quantum Fourier transform (AQFT) in algorithms which involve periodicity estimations. We analyse quantum networks performing AQFT in the presence of decoherence and show that extensive approximations can be made before the accuracy of AQFT (as compared with regular quantum Fourier transform) is compromised. We show that for some computations an approximation may imply a better performance.Comment: 14 pages, 10 fig. (8 *.eps files). More information on http://eve.physics.ox.ac.uk/QChome.html http://www.physics.helsinki.fi/~kasuomin http://www.physics.helsinki.fi/~kira/group.htm

On the bifurcation set of unique expansions

Number theory, Algebra and GeometryAnalysis and Stochastic