Johnson Mwebaze

First discoveries of z~6 quasars with the Kilo Degree Survey and VISTA Kilo-Degree Infrared Galaxy survey

We present the results of our first year of quasar search in the ongoing ESO public Kilo-Degree Survey (KiDS) and VISTA Kilo-Degree Infrared Galaxy (VIKING) surveys. These surveys are among the deeper wide-field surveys that can be used to uncover large numbers of z ˜ 6 quasars. This allows us to probe a more common population of z ˜ 6 quasars that is fainter than the well-studied quasars from the main Sloan Digital Sky Survey. From this first set of combined survey catalogues covering ˜250 deg2 we selected point sources down to ZAB = 22 that had a very red i - Z (i - Z > 2.2) colour. After follow-up imaging and spectroscopy, we discovered four new quasars in the redshift range 5.8 <z <6.0. The absolute magnitudes at a rest-frame wavelength of 1450 A are between -26.6 <M1450 <-24.4, confirming that we can find quasars fainter than M*, which at z = 6 has been estimated to be between M* = -25.1 and M* = -27.6. The discovery of four quasars in 250 deg2 of survey data is consistent with predictions based on the z ˜ 6 quasar luminosity function. We discuss various ways to push the candidate selection to fainter magnitudes and we expect to find about 30 new quasars down to an absolute magnitude of M1450 = -24. Studying this homogeneously selected faint quasar population will be important to gain insight into the onset of the co-evolution of the black holes and their stellar hosts.

Astro-WISE: Tracing and Using Lineage for Scientific Data Processing

Most workflow systems that support data provenance primarily focus on tracing lineage of data. Data provenance by data lineage provides the derivation history of data including information about services and input data that contributed to the creation of a data product. We show that tracing lineage by means of full backward chaining not only enables users to share, discover and reuse the data, but also supports scientific data processing through storage, retrieval and (re)processing of digitized scientific data. In this paper, we present Astro-WISE, a distributed system for processing, analyzing and disseminating wide field imaging astronomical data. We show how Astro-WISE traces lineage of data and how it facilitates data processing, retrieval, storage, archiving. Particularly we show how it solves issues related to the changing data items typical for the scientific environment, such as physical changes in calibrations, our insight in these changes and improved methods for deriving results.

A data lineage model for distributed sub-image processing

An important challenge facing e-Science is the development of scalable systems and analysis techniques that allow client applications to locate data and services in increasingly large-scale distributed environments. e-Science Systems should achieve three main goals: (i) efficient and selective processing of data, (ii) support network collaboration without clogging distribution networks; and (iii) allow transparency of experiments through repeatability and verifiability of experiments. Several systems have addressed limited combinations of these properties, but we address all three in this work. We describe the architecture and implementation of such a framework in Astro-WISE, an astronomical approach to distributed data processing, discovery and retrieval of datasets that achieves scalability via dynamic linking (data lineage) maintained within the system. We show that lineage data collected during the processing and analysis of datasets can be reused to perform selective reprocessing(at sub-image level)ondatasets while the remainder of the dataset is untouched, a rather difficult process to automate without lineage.

Dynamic Pipeline Changes in Scientific Data Processing

Understanding the difference between data objects is a major problem especially in a scientific collaboration which allows scientists to collectively reuse data, modify and adapt scripts developed by their peers to process data while publishing the results to a centralized data store. Although data provenance has been significantly studied to address the origins of a data item, it does not however addresses changes made to the source code. Systems often appear as a large number of modules each containing hundreds of lines of code. It is, in general, not obvious which parts of source code contributed to the change in data object. The paper introduces the Class-Based Object Versioning framework, which overcomes some of the shortcomings of popular versioning systems (e.g. CVS, SVN) in maintaining data and code provenance information in scientific computing environments. The framework automatically identifies and captures useful fine-grained changes in the data and code of scripts that perform scientific experiments so that important information about intermediate stages (i.e. unrecorded changes in experiment parameters and procedures) can be identified and analyzed.

Using Data Lineage for Sub-image Processing

In the paper, we show that lineage data collected during the processing and analysis of datasets can be reused to perform selective reprocessing (at sub-image level) on datasets while the remainder of the dataset is untouched, a rather difficult process to automate without lineage.

Towards a provenance framework for sub-image processing for astronomical data

While there has been advances in observational equipment that generate huge high quality images, the processing of these images remains a major bottleneck. We show that provenance data collected during the processing of data can be reused to perform selective processing of data and support network collaboration without clogging distribution networks. We introduce the idea of sub-image processing (SIMP) in the context of processing a subset of pixels of an image and the use of provenance data to assemble pipelines and to select processing metadata for SIMP. We describe an implementation of SIMP in Astro-WISE