Measurement of Antenna Surfaces from In- and Out-Of-Focus Beam Maps using Astronomical Sources

We present a technique for the accurate estimation of large-scale errors in an antenna surface using astronomical sources and detectors. The technique requires several out-of-focus images of a compact source and the signal-to-noise ratio needs to be good but not unreasonably high. For a given pattern of surface errors, the expected form of such images can be calculated directly. We show that it is possible to solve the inverse problem of finding the surface errors from the images in a stable manner using standard numerical techniques. To do this we describe the surface error as a linear combination of a suitable set of basis functions (we use Zernike polynomials). We present simulations illustrating the technique and in particular we investigate the effects of receiver noise and pointing errors. Measurements of the 15-m James Clerk Maxwell telescope made using this technique are presented as an example. The key result is that good measurements of errors on large spatial scales can be obtained if the input images have a signal-to-noise ratio of order 100 or more. The important advantage of this technique over transmitter-based holography is that it allows measurements at arbitrary elevation angles, so allowing one to characterise the large scale deformations in an antenna as a function of elevation.Comment: 6 pages, 5 figures (accepted by Astronomy & Astrophysics

Noncommutativity relations in type IIB theory and their supersymmetry

In the present paper we investigate noncommutativity of $D9$ and $D5$-brane world-volumes embedded in space-time of type IIB superstring theory. Boundary conditions, which preserve half of the initial supersymmetry, are treated as canonical constraints. Solving the constraints we obtain original coordinates in terms of the effective coordinates and momenta. Presence of momenta induces noncommutativity of string endpoints. We show that noncommutativity relations are connected by N=1 supersymmetry transformations and noncommutativity parameters are components of N=1 supermultiplet

Out-Of-Focus Holography at the Green Bank Telescope

We describe phase-retrieval holography measurements of the 100-m diameter Green Bank Telescope using astronomical sources and an astronomical receiver operating at a wavelength of 7 mm. We use the technique with parameterization of the aperture in terms of Zernike polynomials and employing a large defocus, as described by Nikolic, Hills & Richer (2006). Individual measurements take around 25 minutes and from the resulting beam maps (which have peak signal to noise ratios of 200:1) we show that it is possible to produce low-resolution maps of the wavefront errors with accuracy around a hundredth of a wavelength. Using such measurements over a wide range of elevations, we have calculated a model for the wavefront-errors due to the uncompensated gravitational deformation of the telescope. This model produces a significant improvement at low elevations, where these errors are expected to be the largest; after applying the model, the aperture efficiency is largely independent of elevation. We have also demonstrated that the technique can be used to measure and largely correct for thermal deformations of the antenna, which often exceed the uncompensated gravitational deformations during daytime observing. We conclude that the aberrations induced by gravity and thermal effects are large-scale and the technique used here is particularly suitable for measuring such deformations in large millimetre wave radio telescopes.Comment: 10 pages, 7 figures (accepted by Astronomy & Astrophysics

Science and Technology Review December 2011

This month's issue has the following articles: (1) High-Performance Computing for Energy Innovation - Commentary by Tomas Diaz de la Rubia; (2) Simulating the Next Generation of Energy Technologies - Projects using high-performance computing demonstrate Livermore's computational horsepower and improve the quality of energy solutions and the speed of deployment; (3) ARC Comes into Focus - The Advanced Radiographic Capability, a petawatt-class laser, can penetrate dense objects to reveal material dynamics during National Ignition Facility experiments; (4) A New Method to Track Viral Evolution - A sensitive technique developed at the Laboratory can identify virus mutations that may jump from host to host; and (5) Data for Defense: New Software Finds It Fast - Department of Defense warfighters and planners are using Livermore software systems to extract pertinent information from massive amounts of data

Self-Corrective Dynamic Networks via Decentralized Reverse Computations

The feasibility of large-scale decentralized networks for local computations, as an alternative to big data systems that are often privacy-intrusive, expensive and serve exclusively corporate interests, is usually questioned by network dynamics such as node leaves, failures and rejoins in the network. This is especially the case when decentralized computations performed in a network, such as the estimation of aggregation functions, e.g. summation, are linked to the actual nodes connected in the network, for instance, counting the sum using input values from only connected nodes. Reverse computations are required to maintain a high aggregation accuracy when nodes leave or fail. This paper introduces an autonomic agent-based model for highly dynamic self-corrective networks using decentralized reverse computations. The model is generic and equips the nodes with the capability to disseminate connectivity status updates in the network. Highly resilient agents to the dynamic network migrate to remote nodes and orchestrate reverse computations for each node leave or failure. In contrast to related work, no other computational resources or redundancy are introduced. The self-corrective model is experimentally evaluated using real-world data from a smart grid pilot project under highly dynamic network adjustments that correspond to catastrophic events with up to 50% of the nodes leaving the network. The model is highly agile and modular and is applied to the large-scale decentralized aggregation network of DIAS, the Dynamic Intelligent Aggregation Service, without major structural changes in its design and operations. Results confirm the outstanding improvement in the aggregation accuracy when self-corrective actions are employed with a minimal increase in communication overhead