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Register a new userOverview and stellar statistics of the expected Gaia Catalogue using the Gaia Object Generator
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Aims: An effort has been undertaken to simulate the expected Gaia Catalogue, including the effect of observational errors. A statistical analysis of this simulated Gaia data is performed in order to better understand what can be obtained from the Gaia astrometric mission. This catalogue is used in order to investigate the potential yield in astrometric, photometric and spectroscopic information, and the extent and effect of observational errors on the true Gaia Catalogue. This article is a follow-up to Robin et. al. (2012), where the expected Gaia Catalogue content was reviewed but without the simulation of observational errors. Methods: The Gaia Object Generator (GOG) catalogue is analysed using the Gaia Analysis Tool (GAT), producing a number of statistics on the catalogue. Results: A simulated catalogue of one billion objects is presented, with detailed information on the 523 million individual single stars it contains. Detailed information is provided for the expected errors in parallax, position, proper motion, radial velocity, photometry in the four Gaia bands, and physical parameter determination including temperature, metallicity and line of sight extinction.
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The Galactic Archaeology with HERMES (GALAH) Survey is a massive observational project to trace the Milky Ways history of star formation, chemical enrichment, stellar migration and minor mergers. Using high-resolution (R$simeq$28,000) spectra taken with the High Efficiency and Resolution Multi-Element Spectrograph (HERMES) instrument at the Anglo-Australian Telescope (AAT), GALAH will determine stellar parameters and abundances of up to 29 elements for up to one million stars. Selecting targets from a colour-unbiased catalogue built from 2MASS, APASS and UCAC4 data, we expect to observe dwarfs at 0.3 to 3 kpc and giants at 1 to 10 kpc. This enables a thorough local chemical inventory of the Galactic thin and thick disks, and also captures smaller samples of the bulge and halo. In this paper we present the plan, process and progress as of early 2016 for GALAH survey observations. In our first two years of survey observing we have accumulated the largest high-quality spectroscopic data set at this resolution, over 200,000 stars. We also present the first public GALAH data catalogue: stellar parameters (Teff, log(g), [Fe/H], [alpha/Fe]), radial velocity, distance modulus and reddening for 10680 observations of 9860 Tycho-2 stars that may be included in the first Gaia data release.
Before the publication of the Gaia Catalogue, the contents of the first data release have undergone multiple dedicated validation tests. These tests aim at analysing in-depth the Catalogue content to detect anomalies, individual problems in specific objects or in overall statistical properties, either to filter them before the public release, or to describe the different caveats of the release for an optimal exploitation of the data. Dedicated methods using either Gaia internal data, external catalogues or models have been developed for the validation processes. They are testing normal stars as well as various populations like open or globular clusters, double stars, variable stars, quasars. Properties of coverage, accuracy and precision of the data are provided by the numerous tests presented here and jointly analysed to assess the data release content. This independent validation confirms the quality of the published data, Gaia DR1 being the most precise all-sky astrometric and photometric catalogue to-date. However, several limitations in terms of completeness, astrometric and photometric quality are identified and described. Figures describing the relevant properties of the release are shown and the testing activities carried out validating the user interfaces are also described. A particular emphasis is made on the statistical use of the data in scientific exploitation.
We produce a clean and well-characterised catalogue of objects within 100,pc of the Sun from the G Early Data Release 3. We characterise the catalogue through comparisons to the full data release, external catalogues, and simulations. We carry out a first analysis of the science that is possible with this sample to demonstrate its potential and best practices for its use. The selection of objects within 100,pc from the full catalogue used selected training sets, machine-learning procedures, astrometric quantities, and solution quality indicators to determine a probability that the astrometric solution is reliable. The training set construction exploited the astrometric data, quality flags, and external photometry. For all candidates we calculated distance posterior probability densities using Bayesian procedures and mock catalogues to define priors. Any object with reliable astrometry and a non-zero probability of being within 100,pc is included in the catalogue. We have produced a catalogue of NFINAL objects that we estimate contains at least 92% of stars of stellar type M9 within 100,pc of the Sun. We estimate that 9% of the stars in this catalogue probably lie outside 100,pc, but when the distance probability function is used, a correct treatment of this contamination is possible. We produced luminosity functions with a high signal-to-noise ratio for the main-sequence stars, giants, and white dwarfs. We examined in detail the Hyades cluster, the white dwarf population, and wide-binary systems and produced candidate lists for all three samples. We detected local manifestations of several streams, superclusters, and halo objects, in which we identified 12 members of G Enceladus. We present the first direct parallaxes of five objects in multiple systems within 10,pc of the Sun.
The second $Gaia$ Data Release (DR2) contains astrometric and photometric data for more than 1.6 billion objects with mean $Gaia$ $G$ magnitude $<$20.7, including many Young Stellar Objects (YSOs) in different evolutionary stages. In order to explore the YSO population of the Milky Way, we combined the $Gaia$ DR2 database with WISE and Planck measurements and made an all-sky probabilistic catalogue of YSOs using machine learning techniques, such as Support Vector Machines, Random Forests, or Neural Networks. Our input catalogue contains 103 million objects from the DR2xAllWISE cross-match table. We classified each object into four main classes: YSOs, extragalactic objects, main-sequence stars and evolved stars. At a 90% probability threshold we identified 1,129,295 YSO candidates. To demonstrate the quality and potential of our YSO catalogue, here we present two applications of it. (1) We explore the 3D structure of the Orion A star forming complex and show that the spatial distribution of the YSOs classified by our procedure is in agreement with recent results from the literature. (2) We use our catalogue to classify published $Gaia$ Science Alerts. As $Gaia$ measures the sources at multiple epochs, it can efficiently discover transient events, including sudden brightness changes of YSOs caused by dynamic processes of their circumstellar disk. However, in many cases the physical nature of the published alert sources are not known. A cross-check with our new catalogue shows that about 30% more of the published $Gaia$ alerts can most likely be attributed to YSO activity. The catalogue can be also useful to identify YSOs among future $Gaia$ alerts.
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