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Register a new userUsing Java for distributed computing in the Gaia satellite data processing
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In recent years Java has matured to a stable easy-to-use language with the flexibility of an interpreter (for reflection etc.) but the performance and type checking of a compiled language. When we started using Java for astronomical applications around 1999 they were the first of their kind in astronomy. Now a great deal of astronomy software is written in Java as are many business applications. We discuss the current environment and trends concerning the language and present an actual example of scientific use of Java for high-performance distributed computing: ESAs mission Gaia. The Gaia scanning satellite will perform a galactic census of about 1000 million objects in our galaxy. The Gaia community has chosen to write its processing software in Java. We explore the manifold reasons for choosing Java for this large science collaboration. Gaia processing is numerically complex but highly distributable, some parts being embarrassingly parallel. We describe the Gaia processing architecture and its realisation in Java. We delve into the astrometric solution which is the most advanced and most complex part of the processing. The Gaia simulator is also written in Java and is the most mature code in the system. This has been successfully running since about 2005 on the supercomputer Marenostrum in Barcelona. We relate experiences of using Java on a large shared machine. Finally we discuss Java, including some of its problems, for scientific computing.
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The Gaia Data Release 2 contains the 1st release of radial velocities complementing the kinematic data of a sample of about 7 million relatively bright, late-type stars. Aims: This paper provides a detailed description of the Gaia spectroscopic data processing pipeline, and of the approach adopted to derive the radial velocities presented in DR2. Methods: The pipeline must perform four main tasks: (i) clean and reduce the spectra observed with the Radial Velocity Spectrometer (RVS); (ii) calibrate the RVS instrument, including wavelength, straylight, line-spread function, bias non-uniformity, and photometric zeropoint; (iii) extract the radial velocities; and (iv) verify the accuracy and precision of the results. The radial velocity of a star is obtained through a fit of the RVS spectrum relative to an appropriate synthetic template spectrum. An additional task of the spectroscopic pipeline was to provide 1st-order estimates of the stellar atmospheric parameters required to select such template spectra. We describe the pipeline features and present the detailed calibration algorithms and software solutions we used to produce the radial velocities published in DR2. Results: The spectroscopic processing pipeline produced median radial velocities for Gaia stars with narrow-band near-IR magnitude Grvs < 12 (i.e. brighter than V~13). Stars identified as double-lined spectroscopic binaries were removed from the pipeline, while variable stars, single-lined, and non-detected double-lined spectroscopic binaries were treated as single stars. The scatter in radial velocity among different observations of a same star, also published in DR2, provides information about radial velocity variability. For the hottest (Teff > 7000 K) and coolest (Teff < 3500 K) stars, the accuracy and precision of the stellar parameter estimates are not sufficient to allow selection of appropriate templates. [Abridged]
The Gaia Data Release 2 (DR2): we summarise the processing and results of the identification of variable source candidates of RR Lyrae stars, Cepheids, long period variables (LPVs), rotation modulation (BY Dra-type) stars, delta Scuti & SX Phoenicis stars, and short-timescale variables. In this release we aim to provide useful but not necessarily complete samples of candidates. The processed Gaia data consist of the G, BP, and RP photometry during the first 22 months of operations as well as positions and parallaxes. Various methods from classical statistics, data mining and time series analysis were applied and tailored to the specific properties of Gaia data, as well as various visualisation tools. The DR2 variability release contains: 228904 RR Lyrae stars, 11438 Cepheids, 151761 LPVs, 147535 stars with rotation modulation, 8882 delta Scuti & SX Phoenicis stars, and 3018 short-timescale variables. These results are distributed over a classification and various Specific Object Studies (SOS) tables in the Gaia archive, along with the three-band time series and associated statistics for the underlying 550737 unique sources. We estimate that about half of them are newly identified variables. The variability type completeness varies strongly as function of sky position due to the non-uniform sky coverage and intermediate calibration level of this data. The probabilistic and automated nature of this work implies certain completeness and contamination rates which are quantified so that users can anticipate their effects. This means that even well-known variable sources can be missed or misidentified in the published data. The DR2 variability release only represents a small subset of the processed data. Future releases will include more variable sources and data products; however, DR2 shows the (already) very high quality of the data and great promise for variability studies.
Gaia is ESAs ambitious space astrometry mission the main objective of which is to astrometrically and spectro-photometrically map 1000 Million celestial objects (mostly in our galaxy) with unprecedented accuracy. The announcement of opportunity for the data processing will be issued by ESA late in 2006. The Gaia Data Processing and Analysis Consortium (DPAC) has been formed recently and is preparing an answer. The satellite will downlink close to 100 TB of raw telemetry data over 5 years. To achieve its required accuracy of a few 10s of Microarcsecond astrometry, a highly involved processing of this data is required. In addition to the main astrometric instrument Gaia will host a Radial Velocity instrument, two low-resolution dispersers for multi-color photometry and two Star Mappers. Gaia is a flying Giga Pixel camera. The various instruments each require relatively complex processing while at the same time being interdependent. We describe the overall composition of the DPAC and the envisaged overall architecture of the Gaia data processing system. We shall delve further into the core processing - one of the nine, so-called, coordination units comprising the Gaia processing system.
Gaia is an ambitious space astrometry mission of ESA with a main objective to map the sky in astrometry and photometry down to a magnitude 20 by the end of the next decade. While the mission is built and operated by ESA and an industrial consortium, the data processing is entrusted to a consortium formed by the scientific community, which was formed in 2006 and formally selected by ESA one year later. The satellite will downlink around 100 TB of raw telemetry data over a mission duration of 5 years from which a very complex iterative processing will lead to the final science output: astrometry with a final accuracy of a few tens of microarcseconds, epoch photometry in wide and narrow bands, radial velocity and spectra for the stars brighter than 17 mag. We discuss the general principles and main difficulties of this very large data processing and present the organisation of the European Consortium responsible for its design and implementation.
The Gaia mission started its regular observing program in the summer of 2014, and since then it is regularly obtaining observations of asteroids. This paper draws the outline of the data processing for Solar System objects, and in particular on the daily short-term processing, from the on-board data acquisition to the ground-based processing. We illustrate the tools developed to compute predictions of asteroid observations, we discuss the procedures implemented by the daily processing, and we illustrate some tests and validations of the processing of the asteroid observations. Our findings are overall consistent with the expectations concerning the performances of Gaia and the effectiveness of the developed software for data reduction.
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