The Gaia mission
2.5 The Gaia Data Processing and Analysis Consortium
timescale variability results, be it in terms of detection or in terms of characterization (de-termination of the characteristic variation timescales and periods if relevant, classification, etc...).
Hence, with the lastGaiaData Release, and the associated per-CCD data with finalized calibration, the completeness of the produced short timescale variable candidates sample should be drastically improved. Moreover, since theGaiashort timescale analysis requires a minimum number of transits, with each Data Release and the acquisition of data on longer timespan up to the 5yrs nominal mission duration, the short timescale module will explore more and more sources over time.
Thanks to the progressive enhancement of both photometric data and astrometric so-lution along the successive Data Releases, the precise positioning of the identified candi-dates in the Gaia HR diagram should help determining their variability type. Similarly, with the low resolution BP and RP spectra and medium resolution RV S spectra from GDR3, the spectral information, radial velocities and astrophysical parameters will bring invaluable clues for classification and further characterization of theGaiashort timescale candidates.
Finally, the aim of theGaiashort timescale variability analysis is, of course, to go as far as possible in the detection and description of the variable candidates found exploiting the whole Gaiadata, but also to point interesting sources showing fast variations to the scientific community, eventually exhibiting very peculiar features e.g. unexpected period or amplitude for the suspected type or unusual spectroscopic features or element abun-dances, for further investigation e.g. with ground-based photometric or spectroscopic follow-up.
2.5 The Gaia Data Processing and Analysis Consortium
As one can see, the exploitation of the whole potential of the Gaia mission includes the acquisition, processing and analysis of a huge amount of complex, diverse and ex-tremely precise data, which represents an enormous task in terms of expertise, effort and dedicated computing power. Created in 2006, theGaiaData Processing and Analysis Con-sortium (hereafter DPAC) is a large pan-European conCon-sortium of scientists and software developers, in charge of the whole data processing activities of Gaia. Drawing its mem-bership from over 25 countries, with more than 450 experts involved in the collaboration, the role of DPAC is on the one hand to develop the tools, algorithms and infrastructure needed for the data processing and analysis of Gaia outputs, and on the other hand to prepare the successive releases. The consortium is divided in 9 Coordination Units (CUs), each CU being responsible for a particular aspect of the wholeGaiaprocessing: CU1 for the system architecture, CU2 for the data simulation, CU3 for the core processing, CU4 for the object processing, CU5 for the photometric processing, CU6 for the spectroscopic processing, CU7 for the variability processing, CU8 for the astrophysical parameters and CU9 for the archive and catalogue. The CUs are supported by 6 Data Processing Centres (DPCs): DPCE (ESAC, Madrid), DPCC (CNES, Toulouse), DPCI (Institute of Astronomy, Cambridge), DPCG (Department of astronomy of the University of Geneva), DPCT (INAF - OATo, Torino) and DPCB (BSC and CESCA, Barcelona). Figure 2.8 represents a schematic view of the interactions between the different CUs, together with the supporting DPCs (in red).
The work presented in this thesis has been done in the frame of the CU7 activities, the latter being responsible for all the aspects of variability processing and analysis inGaia us-ing both astrometry, photometry and spectroscopy. The goal here is to detect the variable
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Figure 2.8: GaiaDPAC data flow and interactions between the CUs and DPCs. Credits to ESA and A. G. A. Brown.
2.5. The Gaia Data Processing and Analysis Consortium
astronomical objects observed byGaia, classify them in different variability classes, derive their characteristic parameters for specific variability types, and give a global description of the observed variable phenomena. Paired with the Data Processing Center - Geneva (DPCG), and gathering 60 members with 9 of them in Geneva, CU7 is in tight interaction with most of the other CUs:
• with CU4, for the processing of complex sources, particularly binary stars.
• with CU5, which is responsible for the photometric calibration, hence being a major source of information as photometry is an essential input for variability investiga-tion.
• with CU6, as they are in charge of the spectral products, which can give clues for variability analysis, especially the radial velocities.
• with CU8, whose task is to extract astrophysical parameters from the availableGaia data, which can be essential to the proper identification and characterization of vari-able sources.
• with CU9, which is in charge of the validation of all theGaiaDPAC products, includ-ing the results of the CU7 variability analysis, prior to diffusion to the public.
The CU7 data processing flow is depicted in Figure 2.9, taken from Eyer et al. (2017). At the moment, only the photometric and spectrophotometric data, inG, GBP and GRP are exploited. The first step of the variability analysis is the calculation of a number of basic descriptive and correlation statistics, such as the number of observations, mean, median, skewness, kurtosis, Inter Quartile Range (IQR), etc ... for all the time series, through the Statistical Parameters module. This gives a first overview of the Gaia photometric data, and is used to determine whether some variability is present in the investigated light-curves.
Then, the variability detection itself is divided in two distinct modules: the General Variability Detection (GVD), targeting variability on its whole, and the Special Variabil-ity Detection (SVD), targeting specific variabilVariabil-ity phenomena which may be missed by the generic variability search methods of GVD, and hence involving specially tailored ap-proaches for the considered variability type. GVD analysis is based on multiple criteria, oriented towards different and complementary aspects of variability, and making use of the CU7 statistical parameters as well as of attributes from other CUs. It involves e.g.
hypothesis testing based on p-value thresholds, or classifiers trained on a similar number of constant and variable sources in a given region of the sky. Note that GVD is applied to the whole photometric data set made available to CU7, contrary to SVD module which is applied to a subset of sources preselected based on their prior information. This is a consequence of the fact that, in general, the SVD algorithms are much more demanding in terms of computing ressources than the GVD ones. The variability types targeted by SVD module are: the planetary transits, the short timescale variability (the object of this thesis), and the Solar-like activity.
The variable candidates identified by either GVD or SVD go through the Characteri-zation module, for further analysis and description of their light-curves via period search and modeling of the observed variability.
At that point, the suspected variable sources are processed by the Classification mod-ule, where each candidate is assigned a set of probability for a predefined list of variable types, by mean of statistical classifiers making use of statistical and characterization pa-rameters. This classification is subsequently used by the Specific Object Studies (SOS)
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Figure 2.9: GaiaCU7 general variability processing flow, from Eyer et al. (2017). Elements drawn in grey are those not applied forGaiaDR1.
module, for further analysis. The aim of SOS is to validate the classification results, i.e. to confirm or refute that the most probable variable type of the considered source according to the classifiers is reliable, but also to derive additional parameters specific to the sup-posed variable type. SOS treatment concerns the Cepheid stars, the RR Lyrae stars, the Solar-like variability, the rotational modulation, the flaring stars, the eclipsing binaries, the transient sources, the microlensing events and the exoplanetary transits.
Finally, the Global Variability Studies module describes the global variability zoo as seen throughGaia’s eyes, validating and assessing the quality of all the results derived in CU7. It comprises two submodules: the Quality Assessment, e.g. by performing consis-tency checks between theGaiaresults and external references such asOGLEorHipparcos, and the Bias Estimation, which quantifies the biases of statistics distributions, due to var-ious selection effects, when compared to reference data sets.
The aim of theGaiashort timescale variability detection and characterization is to take advantage of the whole Gaiadata, exploiting both astrometry, photometry (and particu-larly theGper-CCD photometry), spectrophotometry and spectroscopy. It combines clas-sical periodogram-based methods for periodic variability investigation, and particularly the Bartlett approach (Bartlett 1948), with other specific techniques such as the variogram analysis (Eyer & Genton 1999; Roelens et al. 2017). The short timescale variability pro-cessing module also benefits from statistical parameters produced by the CU7 Statistics module, and from peculiar metrics specifically tailored for bring out and validating fast variations. The CU7 short timescale variability processing forGaia Data Release 2 is de-tailed in Chapter 4.
2.5. The Gaia Data Processing and Analysis Consortium
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