Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-23T16:10:45.719Z Has data issue: false hasContentIssue false

RAVE-Gaia and the impact on Galactic archeology

Published online by Cambridge University Press:  07 March 2018

Andrea Kunder*
Affiliation:
Leibniz-Institut für Astrophysik Potsdam (AIP), An der Sternwarte 16, D-14482 Potsdam, Germany email: [email protected] Saint Martin's University, 5000 Abbey Way SE, Lacey, WA 98503, USA
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The new data release (DR5) of the RAdial Velocity Experiment (RAVE) includes radial velocities of 520,781 spectra of 457,588 individual stars, of which 215,590 individual stars are released in the Tycho-Gaia astrometric solution (TGAS) in Gaia DR1. Therefore, RAVE contains the largest TGAS overlap of the recent and ongoing Milky Way spectroscopic surveys. Most of the RAVE stars also contain stellar parameters (effective temperature, surface gravity, overall metallicity), as well as individual abundances for Mg, Al, Si, Ca, Ti, Fe, and Ni. Combining RAVE with TGAS brings the uncertainties in space velocities down by a factor of 2 for stars in the RAVE volume – 10 km s−1 uncertainties in space velocities are now able to be derived for the majority (70%) of the RAVE-TGAS sample, providing a powerful platform for chemo-dynamic analyses of the Milky Way. Here we discuss the RAVE-TGAS impact on Galactic archaeology as well as how the Gaia parallaxes can be used to break degeneracies within the RAVE spectral regime for an even better return in the derivation of stellar parameters and abundances.

Type
Contributed Papers
Copyright
Copyright © International Astronomical Union 2018 

References

Binney, J., Burnett, B., Kordopatis, G., et al. 2014a, MNRAS, 437, 351 CrossRefGoogle Scholar
Casey, A. R., Hawkins, K., Hogg, D. et al., 2017, ApJ, 840, 59 CrossRefGoogle Scholar
Guiglion, G., de Laverny, P., Recio-Blanco, A. et al., 2016, A&A, 595, 18 Google Scholar
Kordopatis, G., Gilmore, G., Steinmetz, M., et al. 2013, AJ, 146, 134 CrossRefGoogle Scholar
Kunder, A. M., Kordopatis, G., Steinmetz, M. et al., 2017, AJ, 153, 75 CrossRefGoogle Scholar
Lindegren, L., et al. 2016, A&A, 595, 4 Google Scholar
Matijevič, G., Zwitter, T., Bienaymé, O., et al. 2012, ApJS, 200, 14 CrossRefGoogle Scholar
Matijeviĉ, G., Chiappini, C., Grebel, E. K. et al. 2017, arXiv:1704.05695Google Scholar
Minchev, I., Chiappini, C., & Martig, M., 2013, A&A, 558, 9 Google Scholar
Siebert, A., Williams, M. E. K., Siviero, A., et al. 2011, AJ, 141, 187 CrossRefGoogle Scholar
Steinmetz, M., Zwitter, T., Siebert, A., et al. 2006, AJ, 132, 1645 (DR1)CrossRefGoogle Scholar
Valentini, M., Chiappini, C., Davies, G.R. et al. A&A, 600, 66 Google Scholar
Zwitter, T., Siebert, A., Munari, U., et al. 2008, AJ, 136, 421 CrossRefGoogle Scholar