A spectroscopic census of L Dwarfs observed by Gaia
Authors/Creators
- 1. Centre for Astrophysics Research, School of Physics, Astronomy and Mathematics, University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK
- 2. INAF/Osservatorio Astrofisico di Torino, Strada Osservatorio 20, I-10025 Pino Torinese, Italy
- 3. Landessternwarte Königsuhl, 12 D-69117, Heidelberg, Germany
- 4. Instituto de Física y Astronomía, Universidad de Valparaíso, Ave. Gran Bretaña 1111, Valparaíso, Chile; The Millennium Institute of Astrophysics, 7500011, Santiago, Chile
- 5. Instituto de Astrofísica de Canarias, Via Láctea s/n, 38205, La Laguna, Tenerife, Spain; Departamento de Astrofísica, Universidad de La Laguna, 38206, La Laguna, Tenerife, Spain; Grantecan S. A., Centro de Astrofísica de La Palma, Cuesta de San José, E-38712 Breña Baja, La Palma, Spain
Description
The ESA cornerstone mission Gaia will revolutionise astronomy observing objects as diverse as minor planets, stars, and galaxies out to QSOs. We estimate that Gaia will observe directly 500 L0 to L4 dwarfs and a handful ofbr /> L5 to T1 dwarfs, providing precision of 0.1-0.3 mas in parallax for these objects, and tangential velocities at thenbsp;level of 10-30 m s-1. As these objects are very close, the perspective acceleration will change both the parallaxnbsp;and the proper motion over the time frame of the mission, leading to ldquo;astrometricrdquo; radial velocities with errorsnbsp;of 50 km s-1. However, to fully exploit the extremely accurate and precise astrometric data, it is fundamentalnbsp;to obtain better radial velocities. In this contribution we present the preliminary results from a large campaignnbsp;conducted with OSIRIS on the GTC and X-shooter on the VLT to measure radial velocities for L dwarfs that willnbsp;be observed by Gaia. The spectra will be used to determine radial velocities, spectral indices, identify possiblebr /> unresolved binaries, and further investigate the peculiar objects in the sample. Combined with the Gaia resultsnbsp;this will be a incomparable dataset for many studies./p>
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