Perfect Lattice Topology: The Quantum Rotor as a Test Case

Lattice actions and topological charges that are classically and quantum mechanically perfect (i.e. free of lattice artifacts) are constructed analytically for the quantum rotor. It is demonstrated that the Manton action is classically perfect while the Villain action is quantum perfect. The geometric construction for the topological charge is only perfect at the classical level. The quantum perfect lattice topology associates a topological charge distribution, not just a single charge, with each lattice field configuration. For the quantum rotor with the classically perfect action and topological charge, the remaining cut-off effects are exponentially suppressed.Comment: 12 pages, including two figures. ordinary LaTeX, requires fps.sty; Submitted to Phys. Lett.

A New Class of Group Field Theories for 1st Order Discrete Quantum Gravity

Group Field Theories, a generalization of matrix models for 2d gravity, represent a 2nd quantization of both loop quantum gravity and simplicial quantum gravity. In this paper, we construct a new class of Group Field Theory models, for any choice of spacetime dimension and signature, whose Feynman amplitudes are given by path integrals for clearly identified discrete gravity actions, in 1st order variables. In the 3-dimensional case, the corresponding discrete action is that of 1st order Regge calculus for gravity (generalized to include higher order corrections), while in higher dimensions, they correspond to a discrete BF theory (again, generalized to higher order) with an imposed orientation restriction on hinge volumes, similar to that characterizing discrete gravity. The new models shed also light on the large distance or semi-classical approximation of spin foam models. This new class of group field theories may represent a concrete unifying framework for loop quantum gravity and simplicial quantum gravity approaches.Comment: 48 pages, 4 figures, RevTeX, one reference adde

Gravitational Scattering in the ADD-model at High and Low Energies

Gravitational scattering in the ADD-model is considered at both sub- and transplanckian energies using a common formalism. By keeping a physical cut-off in the KK tower associated with virtual KK exchange, such as the cut-off implied from a finite brane width, troublesome divergences are removed from the calculations in both energy ranges. The scattering behavior depends on three different energy scales: the fundamental Planck mass, the collision energy and the inverse brane width. The result for energies low compared to the effective cut-off (inverse brane width) is a contact-like interaction. At high energies the gravitational scattering associated with the extra dimensional version of Newton's law is recovered

Calibration artefacts in radio interferometry. I. Ghost sources in WSRT data

This work investigates a particular class of artefacts, or ghost sources, in radio interferometric images. Earlier observations with (and simulations of) the Westerbork Synthesis Radio Telescope (WSRT) suggested that these were due to calibration with incomplete sky models. A theoretical framework is derived that validates this suggestion, and provides predictions of ghost formation in a two-source scenario. The predictions are found to accurately match the result of simulations, and qualitatively reproduce the ghosts previously seen in observational data. The theory also provides explanations for many previously puzzling features of these artefacts (regular geometry, PSF-like sidelobes, seeming independence on model flux), and shows that the observed phenomenon of flux suppression affecting unmodelled sources is due to the same mechanism. We demonstrate that this ghost formation mechanism is a fundamental feature of calibration, and exhibits a particularly strong and localized signature due to array redundancy. To some extent this mechanism will affect all observations (including those with non-redundant arrays), though in most cases the ghosts remain hidden below the noise or masked by other instrumental artefacts. The implications of such errors on future deep observations are discussed.Comment: 19 pages, 15 figures, submitted to MNRA

Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction

Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies between brain signals. In the present work, we apply dimensionality reduction techniques based on graph representations of the brain to decode brain activity from real and simulated fMRI datasets. We introduce seven graphs obtained from a) geometric structure and/or b) functional connectivity between brain areas at rest, and compare them when performing dimension reduction for classification. We show that mixed graphs using both a) and b) offer the best performance. We also show that graph sampling methods perform better than classical dimension reduction including Principal Component Analysis (PCA) and Independent Component Analysis (ICA).Comment: 5 pages, GlobalSIP 201