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Register a new userGSP-spec line list for the parametrisation of Gaia -RVS stellar spectra
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The Gaia mission is a magnitude-limited whole-sky survey that collects an impressive quantity of astrometric, spectro-photometric and spectroscopic data. Among all the on-board instruments, the Radial Velocity Spectrometer (RVS) produces millions of spectra up to a magnitude of G$_{RVS} sim 16$. For the brightest RVS targets, stellar atmospheric parameters and individual chemical abundances are automatically estimated by the Generalized Stellar Parametriser - spectroscopy group (GSP-Spec). These data will be published with the third Gaia Data Release. Some major ingredients of the determination of these stellar parameters include the atomic and molecular line lists that are adopted to compute reference synthetic spectra, on which the parametrisation methods rely. We aim to build such a specific line list optimised for the analysis of RVS late-type star spectra. Starting from the Gaia-ESO line lists, we first compared the observed and synthetic spectra of six well-known reference late-type stars in the wavelength range covered by the RVS instrument. We then improved the quality of the atomic data for the transitions presenting the largest mismatches. The new line list is found to produce very high-quality synthetic spectra for the tested reference stars and has thus been adopted within GSP-Spec.
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Gaia Photometric Science Alerts (GPSA) publishes Gaia G magnitudes and Blue Photometer (BP) and Red Photometer (RP) low-resolution epoch spectra of transient events. 27 high-resolution spectra from Gaias Radial Velocity Spectrometer (RVS) of 12 GPSAs have also been published. These 27 RVS epoch spectra are presented next to their corresponding BP and RP epoch spectra in a single place for the first time. We also present one new RVS spectrum of a 13th GPSA that could not be published by the GPSA system. Of the 13 GPSA with RVS spectra, five are photometrically classified as unknown, five as supernovae (three as SN Ia, one as SN II, one as SN IIP), one as a cataclysmic variable, one as a binary microlensing event and one as a young stellar object. The five GPSAs classified as unknown are potential scientific opportunities, while all of them are a preview of the epoch RVS spectra that will be published in Gaias fourth data release.
Among the myriad of data collected by the ESA Gaia satellite, about 150 million spectra will be delivered by the Radial Velocity Spectrometer (RVS) for stars as faint as G_RVS~16. A specific stellar parametrization will be performed for most of these RVS spectra. Some individual chemical abundances will also be estimated for the brightest targets. We describe the different parametrization codes that have been specifically developed or adapted for RVS spectra within the GSP-spec working group of the analysis consortium. The tested codes are based on optimization (FERRE and GAUGUIN), projection (MATISSE) or pattern recognition methods (Artificial Neural Networks). We present and discuss their expected performances in the recovered stellar atmospheric parameters (Teff, log(g), [M/H]) for B- to K- type stars. The performances for the determinations of [alpha/Fe] ratios are also presented for cool stars. For all the considered stellar types, stars brighter than G_RVS~12.5 will be very efficiently parametrized by the GSP-spec pipeline, including solid estimations of [alpha/Fe]. Typical internal errors for FGK metal-rich and metal-intermediate stars are around 40K in Teff , 0.1dex in log(g), 0.04dex in [M/H], and 0.03dex in [alpha/Fe] at G_RVS=10.3. Similar accuracies in Teff and [M/H] are found for A-type stars, while the log(g) derivation is more accurate. For the faintest stars, with G_RVS>13-14, a spectrophotometric Teff input will allow the improvement of the final GSP-spec parametrization. The reported results show that the contribution of the RVS based stellar parameters will be unique in the brighter part of the Gaia survey allowing crucial age estimations, and accurate chemical abundances. This will constitute a unique and precious sample for which many pieces of the Milky Way history puzzle will be available, with unprecedented precision and statistical relevance.
The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) started median-resolution spectroscopic (MRS, R$sim$7500) survey since October 2018. The main scientific goals of MRS, including binary stars, pulsators, and other variable stars are launched with a time-domain spectroscopic survey. However, the systematic errors, including the bias induced from wavelength calibration and the systematic difference between different spectrographs have to be carefully considered during radial velocity measurement. In this work, we provide a technique to correct the systematics in the wavelength calibration based on the relative radial velocity measurements from LAMOST MRS spectra. We show that, for the stars with multi-epoch spectra, the systematic bias which is induced from the exposures of different nights can be well corrected for LAMOST MRS in each spectrograph. And the precision of radial velocity zero-point of multi-epoch time-domain observations reaches below 0.5 km/s . As a by-product, we also give the constant star candidates, which can be the secondary radial-velocity standard star candidates of LAMOST MRS time-domain surveys.
This project aims at exploiting the wide-field and limiting-magnitude capabilities of the LSST to fully characterise the resolved stellar populations in/around six Local Group stellar systems of different morphological type at ~30 to ~400 kpc distance from us. We selected targets that host red giant branch (RGB) stars which are within the reach of Gaia and not yet (all) saturated with the LSST. We will use RR Lyrae stars, Cepheids, SX Phoenicis, delta Scuti stars and Long Period Variables, along with the Color Magnitude Diagram of the resolved stellar populations in these 6 systems to: i) trace their different stellar generations over a spatial extension and with a depth that only the LSST can achieve; ii) measure their distances using variable stars of different type/parent stellar population and the Tip of the RGB; iii) map their 3D structures up to the periphery of their halos; iv) search for tidal streams; and v) study their Star Formation Histories over unprecedented large fractions of their bodies. Our ultimate goals are to provide a complete picture of these nearby stellar systems all the way through to their periphery, and to directly link and cross-calibrate the Gaia and LSST projects.
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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