Multi-Dimensional, Compressible Viscous Flow on a Moving Voronoi Mesh

Numerous formulations of finite volume schemes for the Euler and Navier-Stokes equations exist, but in the majority of cases they have been developed for structured and stationary meshes. In many applications, more flexible mesh geometries that can dynamically adjust to the problem at hand and move with the flow in a (quasi) Lagrangian fashion would, however, be highly desirable, as this can allow a significant reduction of advection errors and an accurate realization of curved and moving boundary conditions. Here we describe a novel formulation of viscous continuum hydrodynamics that solves the equations of motion on a Voronoi mesh created by a set of mesh-generating points. The points can move in an arbitrary manner, but the most natural motion is that given by the fluid velocity itself, such that the mesh dynamically adjusts to the flow. Owing to the mathematical properties of the Voronoi tessellation, pathological mesh-twisting effects are avoided. Our implementation considers the full Navier-Stokes equations and has been realized in the AREPO code both in 2D and 3D. We propose a new approach to compute accurate viscous fluxes for a dynamic Voronoi mesh, and use this to formulate a finite volume solver of the Navier-Stokes equations. Through a number of test problems, including circular Couette flow and flow past a cylindrical obstacle, we show that our new scheme combines good accuracy with geometric flexibility, and hence promises to be competitive with other highly refined Eulerian methods. This will in particular allow astrophysical applications of the AREPO code where physical viscosity is important, such as in the hot plasma in galaxy clusters, or for viscous accretion disk models.Comment: 26 pages, 21 figures. Submitted to MNRA

Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods

Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis. The relevance of these techniques is increasing as current sensory devices are developed with ever higher resolution, and problems involving multimodal data sources become more common. A plethora of feature extraction methods are available in the literature collectively grouped under the field of Multivariate Analysis (MVA). This paper provides a uniform treatment of several methods: Principal Component Analysis (PCA), Partial Least Squares (PLS), Canonical Correlation Analysis (CCA) and Orthonormalized PLS (OPLS), as well as their non-linear extensions derived by means of the theory of reproducing kernel Hilbert spaces. We also review their connections to other methods for classification and statistical dependence estimation, and introduce some recent developments to deal with the extreme cases of large-scale and low-sized problems. To illustrate the wide applicability of these methods in both classification and regression problems, we analyze their performance in a benchmark of publicly available data sets, and pay special attention to specific real applications involving audio processing for music genre prediction and hyperspectral satellite images for Earth and climate monitoring

Subtraction-noise projection in gravitational-wave detector networks

In this paper, we present a successful implementation of a subtraction-noise projection method into a simple, simulated data analysis pipeline of a gravitational-wave search. We investigate the problem to reveal a weak stochastic background signal which is covered by a strong foreground of compact-binary coalescences. The foreground which is estimated by matched filters, has to be subtracted from the data. Even an optimal analysis of foreground signals will leave subtraction noise due to estimation errors of template parameters which may corrupt the measurement of the background signal. The subtraction noise can be removed by a noise projection. We apply our analysis pipeline to the proposed future-generation space-borne Big Bang Observer (BBO) mission which seeks for a stochastic background of primordial GWs in the frequency range $\sim 0.1-1$Hz covered by a foreground of black-hole and neutron-star binaries. Our analysis is based on a simulation code which provides a dynamical model of a time-delay interferometer (TDI) network. It generates the data as time series and incorporates the analysis pipeline together with the noise projection. Our results confirm previous ad hoc predictions which say that BBO will be sensitive to backgrounds with fractional energy densities below $\Omega=10^{-16}$Comment: 54 pages, 15 figure

Gravitational conundrum? Dynamical mass segregation versus disruption of binary stars in dense stellar systems

Upon their formation, dynamically cool (collapsing) star clusters will, within only a few million years, achieve stellar mass segregation for stars down to a few solar masses, simply because of gravitational two-body encounters. Since binary systems are, on average, more massive than single stars, one would expect them to also rapidly mass segregate dynamically. Contrary to these expectations and based on high-resolution Hubble Space Telescope observations, we show that the compact, 15-30 Myr-old Large Magellanic Cloud cluster NGC 1818 exhibits tantalizing hints at the >= 2 sigma level of significance (> 3 sigma if we assume a power-law secondary-to-primary mass-ratio distribution) of an increasing fraction of F-star binary systems (with combined masses of 1.3-1.6 Msun) with increasing distance from the cluster center, specifically between the inner 10 to 20" (approximately equivalent to the cluster's core and half-mass radii) and the outer 60 to 80". If confirmed, this will offer support of the theoretically predicted but thus far unobserved dynamical disruption processes of the significant population of 'soft' binary systems---with relatively low binding energies compared to the kinetic energy of their stellar members---in star clusters, which we have access to here by virtue of the cluster's unique combination of youth and high stellar density.Comment: Accepted for publication in The Astrophysical Journal; 19 pages in AASTeX format; 3 figure

Optimal inference in a class of regression models

We consider the problem of constructing confidence intervals (CIs) for a linear functional of a regression function, such as its value at a point, the regression discontinuity parameter, or a regression coefficient in a linear or partly linear regression. Our main assumption is that the regression function is known to lie in a convex function class, which covers most smoothness and/or shape assumptions used in econometrics. We derive finite-sample optimal CIs and sharp efficiency bounds under normal errors with known variance. We show that these results translate to uniform (over the function class) asymptotic results when the error distribution is not known. When the function class is centrosymmetric, these efficiency bounds imply that minimax CIs are close to efficient at smooth regression functions. This implies, in particular, that it is impossible to form CIs that are tighter using data-dependent tuning parameters, and maintain coverage over the whole function class. We specialize our results to inference on the regression discontinuity parameter, and illustrate them in simulations and an empirical application.Comment: 39 pages plus supplementary material

Weak Lensing by High-Redshift Clusters of Galaxies - I: Cluster Mass Reconstruction

We present the results of a weak lensing survey of six high-redshift (z > 0.5), X-ray selected clusters of galaxies. We have obtained ultra-deep R-band images of each cluster with the Keck Telescope, and have measured a weak lensing signal from each cluster. From the background galaxy ellipticities we create two-dimensional maps of the surface mass density of each cluster. We find that the substructure seen in the mass reconstructions typically agree well with substructure in both the cluster galaxy distributions and X-ray images of the clusters. We also measure the one-dimensional radial profiles of the lensing signals and fit these with both isothermal spheres and "universal" CDM profiles. We find that the more massive clusters are less compact and not as well fit by isothermal spheres as the less massive clusters, possibly indicating that they are still in the process of collapse.Comment: 43 pages, 15 figures, uses aastex, submitted to ApJ 4 color plates produced here as jpg's, larger versions of the jpgs can be found at http://www.mpa-garching.mpg.de/~clow