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Gaia Data Release 2: Observational Hertzsprung-Russell diagrams

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 Added by Carine Babusiaux
 Publication date 2018
  fields Physics
and research's language is English




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We highlight the power of the Gaia DR2 in studying many fine structures of the Hertzsprung-Russell diagram (HRD). Gaia allows us to present many different HRDs, depending in particular on stellar population selections. We do not aim here for completeness in terms of types of stars or stellar evolutionary aspects. Instead, we have chosen several illustrative examples. We describe some of the selections that can be made in Gaia DR2 to highlight the main structures of the Gaia HRDs. We select both field and cluster (open and globular) stars, compare the observations with previous classifications and with stellar evolutionary tracks, and we present variations of the Gaia HRD with age, metallicity, and kinematics. Late stages of stellar evolution such as hot subdwarfs, post-AGB stars, planetary nebulae, and white dwarfs are also analysed, as well as low-mass brown dwarf objects. The Gaia HRDs are unprecedented in both precision and coverage of the various Milky Way stellar populations and stellar evolutionary phases. Many fine structures of the HRDs are presented. The clear split of the white dwarf sequence into hydrogen and helium white dwarfs is presented for the first time in an HRD. The relation between kinematics and the HRD is nicely illustrated. Two different populations in a classical kinematic selection of the halo are unambiguously identified in the HRD. Membership and mean parameters for a selected list of open clusters are provided. They allow drawing very detailed cluster sequences, highlighting fine structures, and providing extremely precise empirical isochrones that will lead to more insight in stellar physics. Gaia DR2 demonstrates the potential of combining precise astrometry and photometry for large samples for studies in stellar evolution and stellar population and opens an entire new area for HRD-based studies.



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The second Gaia data release (Gaia-DR2) contains, beyond the astrometry, three-band photometry for 1.38 billion sources. We have used these three broad bands to infer stellar effective temperatures, Teff, for all sources brighter than G=17 mag with Teff in the range 3000-10 000 K (161 million sources). Using in addition the parallaxes, we infer the line-of-sight extinction, A_G, and the reddening, E[BP-RP], for 88 million sources. Together with a bolometric correction we derive luminosity and radius for 77 million sources. These quantities as well as their estimated uncertainties are part of Gaia-DR2. Here we describe the procedures by which these quantities were obtained, including the underlying assumptions, comparison with literature estimates, and the limitations of our results. Typical accuracies are of order 324 K (Teff), 0.46 mag (A_G), 0.23 mag (E[BP-RP]), 15% (luminosity), and 10% (radius). Being based on only a small number of observable quantities and limited training data, our results are necessarily subject to some extreme assumptions that can lead to strong systematics in some cases (not included in the aforementioned accuracy estimates). One aspect is the non-negativity contraint of our estimates, in particular extinction. Yet in several regions of parameter space our results show very good performance, for example for red clump stars and solar analogues. Large uncertainties render the extinctions less useful at the individual star level, but they show good performance for ensemble estimates. We identify regimes in which our parameters should and should not be used and we define a clean sample. Despite the limitations, this is the largest catalogue of uniformly-inferred stellar parameters to date. More precise and detailed astrophysical parameters based on the full BP/RP spectrophotometry are planned as part of the third Gaia data release.
Context. Although the Gaia catalogue on its own is a very powerful tool, it is the combination of this high-accuracy archive with other archives that will truly open up amazing possibilities for astronomical research. The advanced interoperation of archives is based on cross-matching, leaving the user with the feeling of working with one single data archive. The data retrieval should work not only across data archives but also across wavelength domains. The first step for a seamless access to the data is the computation of the cross-match between Gaia and external surveys. Aims. We describe the adopted algorithms and results of the pre-computed cross-match of the Gaia Data Release 2 (DR2) catalogue with dense surveys (Pan-STARRS1 DR1, 2MASS, SDSS DR9, GSC 2.3, URAT-1, allWISE, PPMXL, and APASS DR9) and sparse catalogues (Hipparcos2, Tycho-2, and RAVE 5). Methods. A new algorithm is developed specifically for sparse catalogues. Improvements and changes with respect to the algorithm adopted for DR1 are described in detail. Results. The outputs of the cross-match are part of the official Gaia DR2 catalogue. The global analysis of the cross-match results is also presented.
As the opening review to the focus meeting ``Stellar Behemoths: Red Supergiants across the Local Universe, I here provide a brief introduction to red supergiants, setting the stage for subsequent contributions. I highlight some recent activity in the field, and identify areas of progress, areas where progress is needed, and how such progress might be achieved.
We present results from the analysis of 401 RR Lyrae stars (RRLs) belonging to the field of the Milky Way (MW). For a fraction of them multi-band ($V$, $K_{rm s}$, $W1$) photometry, metal abundances, extinction values and pulsation periods are available in the literature and accurate trigonometric parallaxes measured by the Gaia mission alongside Gaia $G$-band time-series photometry have become available with the Gaia second data release (DR2) on 2018 April 25. Using a Bayesian fitting approach we derive new near-, mid-infrared period-absolute magnitude-metallicity ($PMZ$) relations and new absolute magnitude-metallicity relations in the visual ($M_V - {rm [Fe/H]}$) and $G$ bands ($M_G - {rm [Fe/H]}$), based on the Gaia DR2 parallaxes. We find the dependence of luminosity on metallicity to be higher than usually found in the literature, irrespective of the passband considered. Running the adopted Bayesian model on a simulated dataset we show that the high metallicity dependence is not caused by the method, but likely arises from the actual distribution of the data and the presence of a zero-point offset in the Gaia parallaxes. We infer a zero-point offset of $-0.057$ mas, with the Gaia DR2 parallaxes being systematically smaller. We find the RR Lyrae absolute magnitude in the $V$, $G$, $K_{rm s}$ and $W1$ bands at metallicity of [Fe/H]=$-1.5$ dex and period of P = 0.5238 days, based on Gaia DR2 parallaxes to be $M_V = 0.66pm0.06$ mag, $M_G = 0.63pm0.08$ mag, $M_{K_{rm s}} = -0.37pm0.11$ mag and $M_{W1} = -0.41pm0.11$ mag, respectively.
139 - F. Arenou , X. Luri , C. Babusiaux 2018
The second Gaia data release (DR2), contains very precise astrometric and photometric properties for more than one billion sources, astrophysical parameters for dozens of millions, radial velocities for millions, variability information for half a million of stellar sources and orbits for thousands of solar system objects. Before the Catalogue publication, these data have undergone dedicated validation processes. The goal of this paper is to describe the validation results in terms of completeness, accuracy and precision of the various Gaia DR2 data. The validation processes include a systematic analysis of the Catalogue content to detect anomalies, either individual errors or statistical properties, using statistical analysis, and comparisons to external data or to models. Although the astrometric, photometric and spectroscopic data are of unprecedented quality and quantity, it is shown that the data cannot be used without a dedicated attention to the limitations described here, in the Catalogue documentation and in accompanying papers. A particular emphasis is put on the caveats for the statistical use of the data in scientific exploitation.
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