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تسجيل مستخدم جديدGaia Data Release 2. Short-timescale variability processing and analysis
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The Gaia DR2 sample of short-timescale variable candidates results from the investigation of the first 22 months of Gaia photometry for a subsample of sources at the Gaia faint end. For this exercise, we limited ourselves to the case of suspected rapid periodic variability. Our study combines fast-variability detection through variogram analysis, high-frequency search by means of least-squares periodograms, and empirical selection based on the investigation of specific sources seen through the Gaia eyes (e.g. known variables or visually identified objects with peculiar features in their light curves). The progressive definition and validation of this selection criterion also benefited from supplementary ground-based photometric monitoring of a few preliminary candidates, performed at the Flemish Mercator telescope (Canary Islands, Spain) between August and November 2017. We publish a list of 3,018 short-timescale variable candidates, spread throughout the sky, with a false-positive rate up to 10-20% in the Magellanic Clouds, and a more significant but justifiable contamination from longer-period variables between 19% and 50%, depending on the area of the sky. Although its completeness is limited to about 0.05%, this first sample of Gaia short-timescale variables recovers some very interesting known short-period variables, such as post-common envelope binaries or cataclysmic variables, and brings to light some fascinating, newly discovered variable sources. In the perspective of future Gaia data releases, several improvements of the short-timescale variability processing are considered, by enhancing the existing variogram and period-search algorithms or by classifying the identified candidates. Nonetheless, the encouraging outcome of our Gaia DR2 analysis demonstrates the power of this mission for such fast-variability studies, and opens great perspectives for this domain of astrophysics.
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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.
The second Gaia data release is based on 22 months of mission data with an average of 0.9 billion individual CCD observations per day. A data volume of this size and granularity requires a robust and reliable but still flexible system to achieve the
demanding accuracy and precision constraints that Gaia is capable of delivering. The internal Gaia photometric system was initialised using an iterative process that is solely based on Gaia data. A set of calibrations was derived for the entire Gaia DR2 baseline and then used to produce the final mean source photometry. The photometric catalogue contains 2.5 billion sources comprised of three different grades depending on the availability of colour information and the procedure used to calibrate them: 1.5 billion gold, 144 million silver, and 0.9 billion bronze. These figures reflect the results of the photometric processing; the content of the data release will be different due to the validation and data quality filters applied during the catalogue preparation. The photometric processing pipeline, PhotPipe, implements all the processing and calibration workflows in terms of Map/Reduce jobs based on the Hadoop platform. This is the first example of a processing system for a large astrophysical survey project to make use of these technologies. The improvements in the generation of the integrated G-band fluxes, in the attitude modelling, in the cross-matching, and and in the identification of spurious detections led to a much cleaner input stream for the photometric processing. This, combined with the improvements in the definition of the internal photometric system and calibration flow, produced high-quality photometry. Hadoop proved to be an excellent platform choice for the implementation of PhotPipe in terms of overall performance, scalability, downtime, and manpower required for operations and maintenance.
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 ESA Gaia mission provides a unique time-domain survey for more than one billion sources brighter than G=20.7 mag. Gaia offers the unprecedented opportunity to study variability phenomena in the Universe thanks to multi-epoch G-magnitude photometr
y in addition to astrometry, blue and red spectro-photometry, and spectroscopy. Within the Gaia Consortium, Coordination Unit 7 has the responsibility to detect variable objects, classify them, derive characteristic parameters for specific variability classes, and provide global descriptions of variable phenomena. We describe the variability processing and analysis that we plan to apply to the successive data releases, and we present its application to the G-band photometry results of the first 14 months of Gaia operations that comprises 28 days of Ecliptic Pole Scanning Law and 13 months of Nominal Scanning Law. Out of the 694 million, all-sky, sources that have calibrated G-band photometry in this first stage of the mission, about 2.3 million sources that have at least 20 observations are located within 38 degrees from the South Ecliptic Pole. We detect about 14% of them as variable candidates, among which the automated classification identified 9347 Cepheid and RR Lyrae candidates. Additional visual inspections and selection criteria led to the publication of 3194 Cepheid and RR Lyrae stars, described in Clementini et al. (2016). Under the restrictive conditions for DR1, the completenesses of Cepheids and RR Lyrae stars are estimated at 67% and 58%, respectively, numbers that will significantly increase with subsequent Gaia data releases. Data processing within the Gaia Consortium is iterative, the quality of the data and the results being improved at each iteration. The results presented in this article show a glimpse of the exceptional harvest that is to be expected from the Gaia mission for variability phenomena. [abridged]
Aims. We describe the photometric content of the second data release of the Gaia project (Gaia DR2) and its validation along with the quality of the data. Methods. The validation was mainly carried out using an internal analysis of the photometry. Ex
ternal comparisons were also made, but were limited by the precision and systematics that may be present in the external catalogues used. Results. In addition to the photometric quality assessment, we present the best estimates of the three photometric passbands. Various colour-colour transformations are also derived to enable the users to convert between the Gaia and commonly used passbands. Conclusions. The internal analysis of the data shows that the photometric calibrations can reach a precision as low as 2 mmag on individual CCD measurements. Other tests show that systematic effects are present in the data at the 10 mmag level.
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