The Data Management (DM) subsystem of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) is responsible for creating the software, services, and systems that will be used to produce science ready data products. The software, currently under development, is heterogeneous, comprising both C++ and Python components, and is designed to facilitate both the processing of the observatory images and to enable value-added contributions from the broader scientific community. Verification and validation of these software products, services, and systems is an essential yet time-consuming task. In this paper, we present the tooling and procedures developed to ensure a systematic approach to the production of documentation for verification and validation. By adopting a systematic approach, we guarantee full traceability to system requirements, integration with the project’s Systems Engineering model, and substantially reduce the time required for the whole process.
KEYWORDS: Systems modeling, Large Synoptic Survey Telescope, Data modeling, Systems engineering, Integrated modeling, Model-based design, Safety, Telescopes
This paper describes the evolution of the processes, methodologies and tools developed and utilized on the Large Synoptic Survey Telescope (LSST) project that provide a complete end-to-end environment for verification planning, execution, and reporting. LSST utilizes No Magic’s MagicDraw Cameo Systems Modeler tool as the core tool for systems modeling, a Jira-based test case/test procedure/test plan tool called Test Management for Jira for verification execution, and Intercax’s Syndeia tool for bi-directional synchronization of data between Cameo Systems Modeler and Jira. Several additional supporting tools and services are also described to round out a complete solution. The paper describes the project’s needs, overall software platform architecture, and customizations developed to provide the end to- end solution.
KEYWORDS: Space operations, Optical resolution, Data processing, Software development, Systems engineering, Copper, Image processing, Data storage, Astronomy, Space telescopes
With its launch at the very end of 2013, ESA's astrometry satellite Gaia began its endeavor to compile astrometric and photometric measurements of at least one billion objects, as well as high resolution optical spectra of hundred million objects. The Gaia catalog therefore results in a wealth of coherently determined astrophysical parameters of these objects. After its extensive commissioning phase, Gaia entered the nominal mission phase in July 2014. The science ground segment, which is formed by the Gaia Data Processing and Analysis Consortium (DPAC), has since then started its operations. DPAC is a large, multi-national, science consortium which has to handle and process the dense and complex Gaia data stream. With its decentralized management and its distributed infrastructure, the Gaia DPAC is a remarkable undertaking. In this paper we will summarize some of the experiences of the DPAC facing the real Gaia data, compare this to the pre-launch expectations, and critically review the development phase.
KEYWORDS: Space operations, Telescopes, Space telescopes, Satellites, Data processing, Astronomy, Stars, Electroluminescent displays, Charge-coupled devices, System on a chip
With the successful launch of the next generation space astrometry mission Gaia* in December 2013, this paper is going to provide an overview and status of this mission after its first half year of operations in space. We will provide a summary of the performed commissioning activities, the obtained findings, and how these first months of working on real data is impacting the DPAC operational concepts. The results will also provide a first glimpse of what Gaia will deliver in its future catalog releases.
KEYWORDS: Space operations, Data processing, Copper, Calibration, Charge-coupled devices, System on a chip, Software development, Astronomy, Electroluminescent displays, Photometry
Gaia* is Europe's astrometry satellite which is currently entering its operational phase. The Gaia mission will determine the astrometric, photometric properties, as well as the radial velocities of over one billion stellar objects. The observations collected over 24 hours by Gaia will consist of several tens of, up to more than one hundred, million of imagery data files, and low and high resolution spectra. This avalanche of data will be handled by the Gaia Data Processing and Analysis Consortium (DPAC) which is tasked with the processing of the collected data and to ultimately compile the Gaia catalogue. In order to prepare itself for taking up this challenge, DPAC has conducted a number of campaigns simulating its daily operations. Here we will describe these operation rehearsals, their preparation, conduct, and the return of experience. The positive experiences from these campaigns are now being used to also conduct such campaigns for DPAC's long term processing, based on real data.
The Gaia Data Processing and Analysis Consortium (DPAC) is developing the required software to handle and
process the data collected during ESA’s Gaia astronomy mission. DPAC consist of more than 400 scientists
and engineers developing several dozens of large software packages. Such a large software development project
requires adequate progress monitoring techniques. DPAC has developed IMT as a semi automated monitoring
tool. In this paper we will describe the IMT system, the results it provides, and the experiences in view of usage
withing the DPAC management process. Also the potential usage of IMT in other large scientific projects is
discussed.
KEYWORDS: Software development, Copper, Space operations, Standards development, Seaborgium, Data processing, System on a chip, Software engineering, Distance measurement, Stars
The ESA satellite Gaia aims to measure the main astrometric parameters and generate an astrometric catalogue
of 109 objects with an accuracy on the micro-arcsec level. To reach this goal the European scientific community
has formed the Gaia Data Processing and Analysis Consortium (DPAC). DPAC includes the Science Operation
Centre (SOC) at ESAC and together they constitute the Gaia science ground segment, including a total of more
than 400 scientists and engineers. Such a large group of developers represent a massive development effort which
requires effective quality monitoring and assurance mechanisms and reporting structures to be in place. In this
paper we will outline the procedures and mechanisms setup within the consortium to assure that DPAC software
products and the necessary hardware will be ready when they are needed and fulfill their expectations. The
experiences gathered in the employed PA/QA process, which is based on the relevant ECSS standards, will be
described and will prove useful for other projects of similarly large scale.
KEYWORDS: Data processing, Algorithm development, Software development, Systems modeling, Systems engineering, Astronomy, Current controlled current source, Satellites, Copper
Gaia is Europe's future astrometry satellite which is currently under development. The data collected
by Gaia will be treated and analyzed by the "Data Processing and Analysis Consortium" (DPAC). DPAC consists of over 400 scientists in more
than 22 countries, which are currently developing the required data reduction, analysis and handling algorithms and routines. DPAC is organized
in Coordination Units (CU's) and Data Processing Centres (DPCs). Each of these entities is individually responsible for the development of
software for the processing of the different data. In 2008, the DPAC Project Office (PO) has been set-up with the task to manage the day-to-day activities of the consortium including implementation, development and operations. This paper describes the tasks DPAC faces and the role of the DPAC PO
in the Gaia framework and how it supports the DPAC entities in their effort to fulfill the Gaia promise.
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