Optical probing of supersonic aerodynamic turbulence

Laser quasi-schlieren system and laser shadow-correlation system retrieve flow-related signals sufficient for computing accurate, reproducible correlation peaks. Statistical method for obtaining one-shot measurements of the decay history of turbulent structures in a stationary frame of reference is discussed

The GeV-TeV Connection in Galactic gamma-ray sources

Recent observations with atmospheric Cherenkov telescope systems such as H.E.S.S. and MAGIC have revealed a large number of new sources of very-high-energy (VHE) gamma-rays from 100 GeV - 100 TeV, mostly concentrated along the Galactic plane. At lower energies (100 MeV - 10 GeV) the satellite-based instrument EGRET revealed a population of sources clustering along the Galactic Plane. Given their adjacent energy bands a systematic correlation study between the two source catalogues seems appropriate. Here, the populations of Galactic sources in both energy domains are characterised on observational as well as on phenomenological grounds. Surprisingly few common sources are found in terms of positional coincidence and spectral consistency. These common sources and their potential counterparts and emission mechanisms will be discussed in detail. In cases of detection only in one energy band, for the first time consistent upper limits in the other energy band have been derived. The EGRET upper limits are rather unconstraining due to the sensitivity mismatch to current VHE instruments. The VHE upper limits put strong constraints on simple power-law extrapolation of several of the EGRET spectra and thus strongly suggest cutoffs in the unexplored energy range from 10 GeV - 100 GeV. Physical reasons for the existence of cutoffs and for differences in the source population at GeV and TeV energies will be discussed. Finally, predictions will be derived for common GeV - TeV sources for the upcoming GLAST mission bridging for the first time the energy gap between current GeV and TeV instruments.Comment: (1) Kavli Institute for Particle Astrophysics and Cosmology (KIPAC), Stanford, USA (2) Stanford University, W.W. Hansen Experimental Physics Lab (HEPL) and KIPAC, Stanford, USA (3) ICREA & Institut de Ciencies de l'Espai (IEEC-CSIC) Campus UAB, Fac. de Ciencies, Barcelona, Spain. (4) School of Physics and Astronomy, University of Leeds, UK. Paper Submitted to Ap

A future very-high-energy view of our Galaxy

The survey of the inner Galaxy with H.E.S.S. was remarkably successful in detecting a wide range of new very-high-energy gamma-ray sources. New TeV gamma-ray emitting source classes were established, although several of the sources remain unidentified, and progress has been made in understanding particle acceleration in astrophysical sources. In this work, we constructed a model of a population of such very-high-energy gamma-ray emitters and normalised the flux and size distribution of this population model to the H.E.S.S.-discovered sources. Extrapolating that population of objects to lower flux levels we investigate what a future array of imaging atmospheric telescopes (IACTs) such as AGIS or CTA might detect in a survey of the Inner Galaxy with an order of magnitude improvement in sensitivity. The sheer number of sources detected together with the improved resolving power will likely result in a huge improvement in our understanding of the populations of galactic gamma-ray sources. A deep survey of the inner Milky Way would also support studies of the interstellar diffuse gamma-ray emission in regions of high cosmic-ray density. In the final section of this paper we investigate the science potential for the Galactic Centre region for studying energy-dependent diffusion with such a future array.Comment: Proceeding of "Heidelberg International Symposium on High Energy Gamma-Ray Astronomy", held in Heidelberg, 7-11 July 2008, submitted to AIP Conference Proceedings. 4 pages, 4 figure

TVL₁shape approximation from scattered 3D data

With the emergence in 3D sensors such as laser scanners and 3D reconstruction from cameras, large 3D point clouds can now be sampled from physical objects within a scene. The raw 3D samples delivered by these sensors however, contain only a limited degree of information about the environment the objects exist in, which means that further geometrical high-level modelling is essential. In addition, issues like sparse data measurements, noise, missing samples due to occlusion, and the inherently huge datasets involved in such representations makes this task extremely challenging. This paper addresses these issues by presenting a new 3D shape modelling framework for samples acquired from 3D sensor. Motivated by the success of nonlinear kernel-based approximation techniques in the statistics domain, existing methods using radial basis functions are applied to 3D object shape approximation. The task is framed as an optimization problem and is extended using non-smooth L1 total variation regularization. Appropriate convex energy functionals are constructed and solved by applying the Alternating Direction Method of Multipliers approach, which is then extended using Gauss-Seidel iterations. This significantly lowers the computational complexity involved in generating 3D shape from 3D samples, while both numerical and qualitative analysis confirms the superior shape modelling performance of this new framework compared with existing 3D shape reconstruction techniques