Scalar Perturbations in Two-Temperature Cosmological Plasmas

We study the properties of density perturbations of a two-component plasma with a temperature difference on a homogeneous and isotropic background. For this purpose we extend the general relativistic gauge invariant covariant (GIC) perturbation theory to include a multi-fluid with a particular equations of state (ideal gas) and imperfect fluid terms due to the relative energy flux between the two species. We derive closed sets of GIC vector and subsequently scalar evolution equations. We then investigate solutions in different regimes of interest. In particular, we study long wavelength and arbitrary wavelength Langmuir and ion-acoustic perturbations. The harmonic oscillations are superposed on a Jeans type instability. We find a generalised Jeans criterion for collapse in a two-temperature plasma, which states that the species with the largest sound velocity determines the Jeans wavelength. Furthermore, we find that within the limit for gravitational collapse, initial perturbations in either the total density or charge density lead to a growth in the initial temperature difference. These results are relevant for the basic understanding of the evolution of inhomogeneities in cosmological models.Comment: 9 pages. Accepted for publication in MNRAS, 5 April 2006 (submitted 20 Januari 2006

Scattering of magnetosonic waves in a relativistic and an-isotropic magnetised plasma

Gravitational waves (GW) propagating through a magnetised plasma excite low-frequency magnetohydrodynamic (MHD) waves. In this paper we investigate whether these waves can produce observable radio emission at higher frequencies by scattering on an an-isotropic intrinsically relativistic distribution of electrons and positrons in the force-free wind surrounding a double neutron star binary merger. The relativistic particle distribution is assumed to be strictly along the magnetic field lines, while the magneto-plasma streams out at a relativistic speed from the neutron stars. In the case of Compton scattering of an incident MHD wave transverse to the magnetic field, we find that the probability of scattering to both a transverse x-mode and a quasi-transverse Langmuir-o mode is suppressed when the scattered frequency is below the local relativistic gyro-frequency, i.e. when the magnetic field is very strong.Comment: 13 pages, 6 figures (2 color). Accepted for publication in Monthly Notices of the Royal Astronomical Society, MNRAS, to appear on-line mid Marc

Geometry-Driven Detection, Tracking and Visual Analysis of Viscous and Gravitational Fingers

Viscous and gravitational flow instabilities cause a displacement front to break up into finger-like fluids. The detection and evolutionary analysis of these fingering instabilities are critical in multiple scientific disciplines such as fluid mechanics and hydrogeology. However, previous detection methods of the viscous and gravitational fingers are based on density thresholding, which provides limited geometric information of the fingers. The geometric structures of fingers and their evolution are important yet little studied in the literature. In this work, we explore the geometric detection and evolution of the fingers in detail to elucidate the dynamics of the instability. We propose a ridge voxel detection method to guide the extraction of finger cores from three-dimensional (3D) scalar fields. After skeletonizing finger cores into skeletons, we design a spanning tree based approach to capture how fingers branch spatially from the finger skeletons. Finally, we devise a novel geometric-glyph augmented tracking graph to study how the fingers and their branches grow, merge, and split over time. Feedback from earth scientists demonstrates the usefulness of our approach to performing spatio-temporal geometric analyses of fingers.Comment: Published at IEEE Transactions on Visualization and Computer Graphic

Deep Learning Models for River Classification at Sub-Meter Resolutions from Multispectral and Panchromatic Commercial Satellite Imagery

Remote sensing of the Earth's surface water is critical in a wide range of environmental studies, from evaluating the societal impacts of seasonal droughts and floods to the large-scale implications of climate change. Consequently, a large literature exists on the classification of water from satellite imagery. Yet, previous methods have been limited by 1) the spatial resolution of public satellite imagery, 2) classification schemes that operate at the pixel level, and 3) the need for multiple spectral bands. We advance the state-of-the-art by 1) using commercial imagery with panchromatic and multispectral resolutions of 30 cm and 1.2 m, respectively, 2) developing multiple fully convolutional neural networks (FCN) that can learn the morphological features of water bodies in addition to their spectral properties, and 3) FCN that can classify water even from panchromatic imagery. This study focuses on rivers in the Arctic, using images from the Quickbird, WorldView, and GeoEye satellites. Because no training data are available at such high resolutions, we construct those manually. First, we use the RGB, and NIR bands of the 8-band multispectral sensors. Those trained models all achieve excellent precision and recall over 90% on validation data, aided by on-the-fly preprocessing of the training data specific to satellite imagery. In a novel approach, we then use results from the multispectral model to generate training data for FCN that only require panchromatic imagery, of which considerably more is available. Despite the smaller feature space, these models still achieve a precision and recall of over 85%. We provide our open-source codes and trained model parameters to the remote sensing community, which paves the way to a wide range of environmental hydrology applications at vastly superior accuracies and 2 orders of magnitude higher spatial resolution than previously possible.Comment: 21 pages, 10 figures, 3 table