Gaia Data Release 3 contains a wealth of new data products for the community. Astrophysical parameters are a major component of this release. They were produced by the Astrophysical parameters ...inference system (Apsis) within the Gaia Data Processing and Analysis Consortium. The aim of this paper is to describe the overall content of the astrophysical parameters in Gaia Data Release 3 and how they were produced. In Apsis we use the mean BP/RP and mean RVS spectra along with astrometry and photometry, and we derive the following parameters: source classification and probabilities for 1.6 billion objects, interstellar medium characterisation and distances for up to 470 million sources, including a 2D total Galactic extinction map, 6 million redshifts of quasar candidates and 1.4 million redshifts of galaxy candidates, along with an analysis of 50 million outlier sources through an unsupervised classification. The astrophysical parameters also include many stellar spectroscopic and evolutionary parameters for up to 470 million sources. These comprise Teff, logg, and m_h (470 million using BP/RP, 6 million using RVS), radius (470 million), mass (140 million), age (120 million), chemical abundances (up to 5 million), diffuse interstellar band analysis (0.5 million), activity indices (2 million), H-alpha equivalent widths (200 million), and further classification of spectral types (220 million) and emission-line stars (50 thousand). This catalogue is the most extensive homogeneous database of astrophysical parameters to date, and it is based uniquely on Gaia data.
Large surveys producing tera- and petabyte-scale databases require machine-learning and knowledge discovery methods to deal with the overwhelming quantity of data and the difficulties of extracting ...concise, meaningful information with reliable assessment of its uncertainty. This study investigates the potential of a few machine-learning methods for the automated analysis of eclipsing binaries in the data of such surveys. We aim to aid the extraction of samples of eclipsing binaries from such databases and to provide basic information about the objects. We estimate class labels according to two classification systems, one based on the light curve morphology (EA/EB/EW classes) and the other based on the physical characteristics of the binary system (system morphology classes; detached through overcontact systems). Furthermore, we explore low-dimensional surfaces along which the light curves of eclipsing binaries are concentrated, to use in the characterization of the binary systems and in the exploration of biases of the full unknown Gaia data with respect to the training sets. We explore the performance of principal component analysis (PCA), linear discriminant analysis (LDA), random forest classification and self-organizing maps (SOM). We pre-process the photometric time series by combining a double Gaussian profile fit and a smoothing spline, in order to de-noise and interpolate the observed light curves. We achieve further denoising, and selected the most important variability elements from the light curves using PCA. We perform supervised classification using random forest and LDA based on the PC decomposition, while SOM gives a continuous 2-dimensional manifold of the light curves arranged by a few important features. We estimate the uncertainty of the supervised methods due to the specific finite training set using ensembles of models constructed on randomized training sets.
The advent of large scale multi-epoch surveys raises the need for automated light curve (LC) processing. This is particularly true for eclipsing binaries (EBs), which form one of the most populated ...types of variable objects. The Gaia mission, launched at the end of 2013, is expected to detect of the order of few million EBs over a 5-year mission. We present an automated procedure to characterize EBs based on the geometric morphology of their LCs with two aims: first to study an ensemble of EBs on a statistical ground without the need to model the binary system, and second to enable the automated identification of EBs that display atypical LCs. We model the folded LC geometry of EBs using up to two Gaussian functions for the eclipses and a cosine function for any ellipsoidal-like variability that may be present between the eclipses. The procedure is applied to the OGLE-III data set of EBs in the Large Magellanic Cloud (LMC) as a proof of concept. The bayesian information criterion is used to select the best model among models containing various combinations of those components, as well as to estimate the significance of the components. Based on the two-Gaussian models, EBs with atypical LC geometries are successfully identified in two diagrams, using the Abbe values of the original and residual folded LCs, and the reduced \(\chi^2\). Cleaning the data set from the atypical cases and further filtering out LCs that contain non-significant eclipse candidates, the ensemble of EBs can be studied on a statistical ground using the two-Gaussian model parameters. For illustration purposes, we present the distribution of projected eccentricities as a function of orbital period for the OGLE-III set of EBs in the LMC, as well as the distribution of their primary versus secondary eclipse widths.
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: 228'904 RR Lyrae stars, 11'438 Cepheids, 151'761 LPVs, 147'535 stars with rotation modulation, 8'882 delta Scuti & SX Phoenicis stars, and 3'018 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 550'737 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.
Karst springs are abundant in Hungary, and many are thermal (temperatures >30°). As thermal springs are a significant part of Hungary's water resources, it is important to quantify their travel times ...in the karst systems. Thus, we chose to measure T and δ18O in the water and δ13C and 14C in dissolved inorganic carbon (DIC) in water from 50 thermal and nonthermal springs and wells in the Bükk Mountains, northeastern Hungary. Environmental isotope data confirm the karst water-flow pattern implied by earlier studies. We found the water in warm springs and boreholes to be mixtures of cold young and old thermal water. We also determined short mean-residence times for some large cold springs. The 14C activities measured in these springs indicate that the recharge area of the karst aquifer is open to the atmosphere, and atmospheric CO2 contributes to the 14C activity of these groundwaters. We observed good correlation between 14C and 3H activities and we determined negative correlations between 14C concentration and δ13C values and temperature. From the δ18O values of the oldest thermal waters, we attribute their origin to precipitation during colder temperatures than at present.
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 photometry 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
The authors present the case of a patient suffering from acute intermittent porphyria presenting as encephalitis. The differential diagnostics, the causes and the therapy of the porphyria attack are ...discussed in detail.
The ESA Gaia mission will provide a multi-epoch database for a billion of objects, including variable objects that comprise stars, active galactic nuclei and asteroids. We highlight a few of Gaia’s ...properties that will benefit the study of variable objects, and illustrate with two examples the work being done in the preparation of the data processing and object characterization. The first example relates to the analysis of the nearly simultaneous multi-band data of Gaia with the Principal Component Analysis techniques, and the second example concerns the classification of Gaia time series into variability types. The results of the ground-based processing of Gaia’s variable objects data will be made available to the scientific community through the intermediate and final ESA releases throughout the mission.
We propose a robust principal component analysis (PCA) framework for the exploitation of multi-band photometric measurements in large surveys. Period search results are improved using the time series ...of the first principal component due to its optimized signal-to-noise ratio.The presence of correlated excess variations in the multivariate time series enables the detection of weaker variability. Furthermore, the direction of the largest variance differs for certain types of variable stars. This can be used as an efficient attribute for classification. The application of the method to a subsample of Sloan Digital Sky Survey Stripe 82 data yielded 132 high-amplitude Delta Scuti variables. We found also 129 new RR Lyrae variables, complementary to the catalogue of Sesar et al., 2010, extending the halo area mapped by Stripe 82 RR Lyrae stars towards the Galactic bulge. The sample comprises also 25 multiperiodic or Blazhko RR Lyrae stars.
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