Kornelis Begeman

DISTRIBUTION OF DARK MATTER IN THE SPIRAL GALAXY NGC-3198

Two-component mass models, consisting of an exponential disk and a spherical halo, are constructed to fit a newly determined rotation curve of NGC 3198 that extends to 11 disk scale lengths. The amount of dark matter inside the last point of the rotation curve, at 30 kpc, is at least 4 times larger than the amount of visible matter, with M/L(B)tot = 18 solar M/L(B). The maximum mass-to-light ratio for the disk is M/L(B) = 3.6. The available data cannot discriminate between disk models with low M/L and high M/L, but arguments are presented which suggest that the true mass-to-light ratio of the disk is close to the maximum computed value. The core radius of the distribution of dark matter is found to satisfy R(core) of between 1.7 and 12.5 kpc. 31 references.

OmegaCAM: wide-field imaging with fine spatial resolution

OmegaCAM is the wide-field camera for the VLT Survey Telescope being completed for ESOs Paranal observatory. The instrument, as well as the telescope, have been designed for very good, natural seeing-limited image quality over a 1 degree field. At the heart of the project are a square-foot photometric shutter, a 12-filter storage/exchange mechanism, a 16k x 16k CCD detector mosaic, and plenty of software for instrument control and data handling, analysis and archiving.

The Astro-WISE datacentric information system

In this paper we present the various concepts behind the Astro-WISE Information System. The concepts form a blueprint for general scientific information systems (WISE) which can satisfy a wide and challenging range of requirements for the data dissemination, storage and processing for various fields in science. We review the main features of the information system and its practical implementation.

Merging Grid Technologies

This paper reports the integration of the astronomical Grid solution realised in the Astro-WISE information system with the EGEE Grid and the porting of Astro-WISE applications on EGEE. We review the architecture of the Astro-WISE Grid, define the problems for the integration of the Grid infrastructures and our solution to these problems. We give examples of applications running on Astro-WISE and EGEE and review future development of the merged system.

OmegaCAM: the 16k×16k CCD camera for the VLT survey telescope

OmegaCAM, a 16k×16k-pixel wide field optical camera, and the VLT Survey Telescope (VST) that is to host it, will constitute a major sky surveying machine that becomes operational in 2004 at ESO’s Paranal Observatory. It maps one square degree of sky with 0.21 arcsec sized pixels. Both individual programs, including monitoring programs, and large sky survey programs are planned. Here we present the integrated design of the VST-OmegaCAM survey machine, including the hardware (large filters and shutter, cf(4836-34)), the VLT compliant control software (cf(4848-10)) and the strongly procedurized observing and calibration strategies. The strict data taking procedures facilitate pipeline data reduction procedures both for the calibration and the science data. In turn, the strongly procedurized data handling allows European-wide federations of data-products. The ASTRO-WISE consortium aims to provide a survey system that makes this possible. On-the-fly re-processing of archival data on the request of individual users with their own plug-ins or newly derived calibrations sets are facilitated in an internationally distributed system. Compared to the classical more static wide-field image archives the newly designed system is characterized by a much more dynamical type of archiving.

LOFAR Information System

In this paper, we present a newly designed and implemented scientific information system for the LOFAR Long Term Archive. It is a distributed multi-tier storage and data processing system that allows a number of users to process Petabytes of data. The LOFAR Information System is designed on the base of the Astro-WISE Information System. The system allows the use of the computing and storage resources of the BiGGrid combined with the metadata database along with the data access and data processing interfaces of Astro-WISE. The architecture of the system, problems solved during the implementation and scientific use cases for the system are also described.

Monitoring the photometric behavior of OmegaCAM with Astro-WISE

The OmegaCAM wide-field optical imager is the sole instrument on the VLT Survey Telescope at ESO’s Paranal Observatory. The instrument, as well as the telescope, have been designed for surveys with very good, natural seeing-limited image quality over a 1 square degree field. OmegaCAM was commissioned in 2011 and has been observing three ESO Public Surveys in parallel since October 15, 2011. We use the Astro-WISE information system to monitor the calibration of the observatory and to produce the Kilo Degree Survey (KiDS). Here we describe the photometric monitoring procedures in Astro-WISE and give a first impression of OmegaCAM’s photometric behavior as a function of time. The long-term monitoring of the observatory goes hand in hand with the KiDS survey production in Astro-WISE. KiDS is observed under partially non-photometric conditions. Based on the first year of OmegaCAM operations it is expected that a ∼ 1–2 % photometric homogeneity will be achieved for KiDS.

Target and (Astro-)WISE technologies - Data federations and its applications

After its first implementation in 2003 the Astro-WISE technology has been rolled out in several European countries and is used for the production of the KiDS survey data. In the multi-disciplinary Target initiative this technology, nicknamed WISE technology, has been further applied to a large number of projects. Here, we highlight the data handling of other astronomical applications, such as VLT-MUSE and LOFAR, together with some non-astronomical applications such as the medical projects Lifelines and GLIMPS, the MONK handwritten text recognition system, and business applications, by amongst others, the Target Holding. We describe some of the most important lessons learned and describe the application of the data-centric WISE type of approach to the Science Ground Segment of the Euclid satellite.

WISE TECHNOLOGY FOR HANDLING BIG DATA FEDERATIONS

This paper presents a Near-Real-Time multi-GPU accelerated solution of the ωk Algorithm for Synthetic Aperture Radar (SAR) data focusing, obtained in Stripmap SAR mode. Starting from an input raw data, the algorithm subdivides it in a grid of a configurable number of bursts along track. A multithreading CPU-side support is made available in order to handle each graphic device in parallel. Then each burst is assigned to a separate GPU and processed including Range Compression, Stolt Mapping via ChirpZ and Azimuth Compression steps. We prove the efficiency of our algorithm by using Sentinel-1 raw data (approx. 3.3 GB) on a commodity graphics card; the single-GPU solution is approximately 4x faster than the industrial multi-core CPU implementation (General ACS SAR Processor, GASP), without significant loss of quality. Using a multi-GPU system, the algorithm is approximately 6x faster with respect to the CPU processor.For decades, field help in case of disasters on the Earth’s surface - like floods, fires or earthquakes - is supported by the analysis of remotely sensed data. In recent years, the monitoring of vehicles, buildings or areas fraught with risk has become another major task for satellite-based crisis intervention. Since these scenarios are unforeseen and time-critical, they require a fast and well coordinated reaction. If useful information is extracted out of image data in realtime directly on board a spacecraft, the timespan between image acquisition and an appropriate reaction can be shortened significantly. Furthermore, on board image analysis allows data of minor interest, e.g. cloud-contaminated scenes, to be discarded and/or treated with lower priority, which leads to an optimized usage of storage and downlink capacity. This paper describes the modular application framework of VIMOS, an on board image processing experiment for remote sensing applications. Special focus will be on resource management, safety and modular commandability.Gaia is an ESA cornerstone mission, which was successfully launched December 2013 and commenced operations in July 2014. Within the Gaia Data Processing and Analysis consortium, Coordination Unit 7 (CU7) is responsible for the variability analysis of over a billion celestial sources and nearly 4 billion associated time series (photometric, spectrophotometric, and spectroscopic), encoding information in over 800 billion observations during the 5 years of the mission, resulting in a petabyte scale analytical problem. In this article, we briefly describe the solutions we developed to address the challenges of time series variability analysis: from the structure for a distributed data-oriented scientific collaboration to architectural choices and specific components used. Our approach is based on Open Source components with a distributed, partitioned database as the core to handle incrementally: ingestion, distributed processing, analysis, results and export in a constrained time window.The seamless mosaicing of massive very high resolution imagery addresses several aspects related to big data from space. Data volume is directly proportional to the size the input data, i.e., order of several TeraPixels for a continent. Data velocity derives from the fact that the input data is delivered over several years to meet maximum cloud contamination constraints with the considered satellites. Data variety results from the need to collect and integrate various ancillary data for cloud detection, land/sea mask delineation, and adaptive colour balancing. This paper details how these 3 aspects of big data are handled and illustrates them for the creation of a seamless pan-European mosaic from 2.5m imagery (Land Monitoring/Urban Atlas Copernicus CORE 03 data set).The current development of satellite imagery means that a great volume of images acquired globally has to be understood in a fast and precise manner. Processing this large quantity of information comes at the cost of finding unsupervised algorithms to fulfill these tasks. Change detection is one of the main issues when talking about the analysis of satellite image time series (SITS). In this paper, we propose a method to analyze changes in SITS based on binary descriptors and on the Hamming distance, regarded as a similarity metric. In order to render an automatic and completely unsupervised technique towards solving this problem, the obtained distances are quantized into change levels using the Lloyd-Max’s algorithm. The experiments are carried on 11 Landsat images at 30 meters spatial resolution, covering an area of approximately 59 × 51 km2 over the surroundings of Bucharest, Romania, and containing information from six subbands of frequency.The Euclid Archive System prototype is a functional information system which is used to address the numerous challenges in the development of fully functional data processing system for Euclid. The prototype must support the highly distributed nature of the Euclid Science Ground System, with Science Data Centres in at least eight countries. There are strict requirements both on data quality control and traceability of the data processing. Data volumes will be greater than 10 Pbyte, with the actual volume being dependent on the amount of reprocessing required.In the space domain, all scientific and technological developments are accompanied by a growth of the number of data sources. More specifically, the world of observation knows this very strong acceleration and the demand for information processing follows the same pace. To meet this demand, the problems associated with non-interoperability of data must be efficiently resolved upstream and without loss of information. We advocate the use of linked data technologies to integrate heterogeneous and schema-less data that we aim to publish in the 5 stars scale in order to foster their re-use. By proposing the 5 stars data model, Tim Berners-Lee drew the perfect roadmap for the production of high quality linked data. In this paper, we present a technological framework that allows to go from raw, scattered and heterogeneous data to structured data with a well-defined and agreed upon semantics, interlinked with other dataset for their common objects.Reference data sets, necessary to the advancement of the field of object recognition by providing a point of comparison for different algorithms, are prevalent in the field of multimedia. Although sharing the same basic object recognition problem, in the field of remote sensing there is a need for specialized reference data sets. This paper would like to open the topic for discussion, by taking a first attempt at creating a reference data set for a satellite image. In doing so, important differences between annotating photographic and satellite images are highlighted, along with their impact on the creation of a reference data set. The results are discussed with a view toward creating a future methodology for the manual annotation of satellite images.The future atmospheric composition Sentinel missions will generate two orders of magnitude more data than the current missions and the operational processing of these big data is a big challenge. The trace gas retrieval from remote sensing data usually requires high-performance radiative transfer model (RTM) simulations and the RTM are usually the bottleneck for the operational processing of the satellite data. To date, multi-core CPUs and also Graphical Processing Units (GPUs) have been used for highly intensive parallel computations. In this paper, we are comparing multi-core and GPU implementations of an RTM based on the discrete ordinate solution method. With GPUs, we have achieved a 20x-40x speed-up for the multi-stream RTM, and 50x speed-up for the two-stream RTM with respect to the original single-threaded CPU codes. Based on these performance tests, an optimal workload distribution scheme between GPU and CPU is proposed. Finally, we discuss the performance obtained with the multi-core-CPU and GPU implementations of the RTM.The effective use of Big Data in current and future scientific missions requires intelligent data handling systems which are able to interface the user to complicated distributed data collections. We review the WISE Concept of Scientific Information Systems and the WISE solutions for the storage and processing as applied to Big Data.Interactive visual data mining, where the user plays a key role in learning process, has gained high attention in data mining and human-machine communication. However, this approach needs Dimensionality Reduction (DR) techniques to visualize image collections. Although the main focus of DR techniques lays on preserving the structure of the data, the occlusion of images and inefficient usage of display space are their two main drawbacks. In this work, we propose to use Non-negative Matrix Factorization (NMF) to reduce the dimensionality of images for immersive visualization. The proposed method aims to preserve the structure of data and at the same time reduce the occlusion between images by defining regularization terms for NMF. Experimental validations performed on two sets of image collections show the efficiency of the proposed method in respect to controlling the trade-off between structure preserving and less occluded visualization.This article provides a short overview about the TanDEM-X mission, its objectives and the payload ground segment (PGS) based on data management, processing systems and long term archive. Due to the large data volume of the acquired and processed products a main challenge in the operation of the PGS is to handle the required data throughput, which is a new dimension for the DLR PGS. To achieve this requirement, several solutions were developed and coordinated. Some of them were more technical nature whereas others optimized the workflows.Clustering of Earth Observation (EO) images has gained a high amount of attention in remote sensing and data mining. Here, each image is represented by a high-dimensional feature vector which could be computed as the results of coding algorithms of extracted local descriptors or raw pixel values. In this work, we propose to learn the features using discriminative Nonnegative Matrix factorization (DNMF) to represent each image. Here, we use the label of some images to produce new representation of images with more discriminative property. To validate our algorithm, we apply the proposed algorithm on a dataset of Synthetic Aperture Radar (SAR) and compare the results with the results of state-of-the-art techniques for image representation. The results confirm the capability of the proposed method in learning discriminative features leading to higher accuracy in clustering.