Apparent Clustering of Intermediate-redshift Galaxies as a Probe of Dark Energy

We show the apparent redshift-space clustering of galaxies in redshift range of 0.2--0.4 provides surprisingly useful constraints on dark energy component in the universe, because of the right balance between the density of objects and the survey depth. We apply Fisher matrix analysis to the the Luminous Red Galaxies (LRGs) in the Sloan Digital Sky Survey (SDSS), as a concrete example. Possible degeneracies in the evolution of the equation of state (EOS) and the other cosmological parameters are clarified.Comment: 5 pages, 3 figures, Phys.Rev.Lett., replaced with the accepted versio

Ground-State Properties of a Heisenberg Spin Glass Model with a Hybrid Genetic Algorithm

We developed a genetic algorithm (GA) in the Heisenberg model that combines a triadic crossover and a parameter-free genetic algorithm. Using the algorithm, we examined the ground-state stiffness of the $\pm J$ Heisenberg model in three dimensions up to a moderate size range. Results showed the stiffness constant of $\theta = 0$ in the periodic-antiperiodic boundary condition method and that of $\theta \sim 0.62$ in the open-boundary-twist method. We considered the origin of the difference in $\theta$ between the two methods and suggested that both results show the same thing: the ground state of the open system is stable against a weak perturbation.Comment: 11 pages, 5 figure

NMR evidence for the persistence of spin-superlattice above the 1/8 magnetization plateau in SrCu2(BO3)2

We present 11B NMR studies of the 2D frustrated dimer spin system SrCu2(BO3)2 in the field range 27-31 T covering the upper phase boundary of the 1/8 magnetization plateau, identified at 28.4 T. Our data provide a clear evidence that above 28.4 T the spin-superlattice of the 1/8 plateau is modified but does not melt even though the magnetization increases. Although this is precisely what is expected for a supersolid phase, the microscopic nature of this new phase is much more complex. We discuss the field-temperature phase diagram on the basis of our NMR data.Comment: 5 pages, 4 figures, published versio

Primordial Non-Gaussianity and Analytical Formula for Minkowski Functionals of the Cosmic Microwave Background and Large-scale Structure

We derive analytical formulae for the Minkowski Functions of the cosmic microwave background (CMB) and large-scale structure (LSS) from primordial non-Gaussianity. These formulae enable us to estimate a non-linear coupling parameter, f_NL, directly from the CMB and LSS data without relying on numerical simulations of non-Gaussian primordial fluctuations. One can use these formulae to estimate statistical errors on f_NL from Gaussian realizations, which are much faster to generate than non-Gaussian ones, fully taking into account the cosmic/sampling variance, beam smearing, survey mask, etc. We show that the CMB data from the Wilkinson Microwave Anisotropy Probe should be sensitive to |f_NL|\simeq 40 at the 68% confidence level. The Planck data should be sensitive to |f_NL|\simeq 20. As for the LSS data, the late-time non-Gaussianity arising from gravitational instability and galaxy biasing makes it more challenging to detect primordial non-Gaussianity at low redshifts. The late-time effects obscure the primordial signals at small spatial scales. High-redshift galaxy surveys at z>2 covering \sim 10Gpc^3 volume would be required for the LSS data to detect |f_NL|\simeq 100. Minkowski Functionals are nicely complementary to the bispectrum because the Minkowski Functionals are defined in real space and the bispectrum is defined in Fourier space. This property makes the Minksowski Functionals a useful tool in the presence of real-world issues such as anisotropic noise, foreground and survey masks. Our formalism can be extended to scale-dependent f_NL easily.Comment: 16 pages, 5 figures, accepted for publication in ApJ (Vol. 653, 2006

Cosmological model differentiation through weak gravitational lensing

We investigate the potential of weak gravitational lensing maps to differentiate between distinct cosmological models, considering cosmic variance due to a limited map extension and the presence of noise. We introduce a measure of the differentiation between two models under a chosen lensing statistics. That enables one to determine in which circumstances (map size and noise level), and for which lensing measures two models can be differentiated at a desired confidence level. As an application, we simulate convergence maps for three cosmological models (SCDM, OCDM, and $\Lambda$CDM), calculate several lensing analyses for them, and compute the differentiation between the models under these analyses. We use first, second, and higher order statistics, including Minkowski functionals, which provide a complete morphological characterization of the lensing maps. We examine for each lensing measure used how noise affects its description of the convergence, and how this affects its ability to differentiate between cosmological models. Our results corroborate to the valuable use of weak gravitational lensing as a cosmological tool.Comment: 12 pages, 8 figures. Submitted to MNRA

Forty-Four Pass Fibre Optic Loop for Improving the Sensitivity of Surface Plasmon Resonance Sensors

A forty-four pass fibre optic surface plasmon resonance sensor that enhances detection sensitivity according to the number of passes is demonstrated for the first time. The technique employs a fibre optic recirculation loop that passes the detection spot forty- four times, thus enhancing sensitivity by a factor of forty-four. Presently, the total number of passes is limited by the onset of lasing action of the recirculation loop. This technique offers a significant sensitivity improvement for various types of plasmon resonance sensors that may be used in chemical and biomolecule detections.Comment: Submitted for publication; patent disclosure submitte

On the class distribution labelling step sensitivity of co-training

Co-training can learn from datasets having a small number of labelled examples and a large number of unlabelled ones. It is an iterative algorithm where examples labelled in previous iterations are used to improve the classification of examples from the unlabelled set. However, as the number of initial labelled examples is often small we do not have reliable estimates regarding the underlying population which generated the data. In this work we make the claim that the proportion in which examples are labelled is a key parameter to co-training. Furthermore, we have done a series of experiments to investigate how the proportion in which we label examples in each step influences cotraining performance. Results show that co-training should be used with care in challenging domains.IFIP International Conference on Artificial Intelligence in Theory and Practice - Knowledge Acquisition and Data MiningRed de Universidades con Carreras en Informática (RedUNCI

Measuring our universe from galaxy redshift surveys

Galaxy redshift surveys have achieved significant progress over the last couple of decades. Those surveys tell us in the most straightforward way what our local universe looks like. While the galaxy distribution traces the bright side of the universe, detailed quantitative analyses of the data have even revealed the dark side of the universe dominated by non-baryonic dark matter as well as more mysterious dark energy (or Einstein's cosmological constant). We describe several methodologies of using galaxy redshift surveys as cosmological probes, and then summarize the recent results from the existing surveys. Finally we present our views on the future of redshift surveys in the era of Precision Cosmology.Comment: 82 pages, 31 figures, invited review article published in Living Reviews in Relativity, http://www.livingreviews.org/lrr-2004-