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Unveiling the stellar halo with TGAS

Published online by Cambridge University Press:  07 March 2018

Jovan Veljanoski ,
L. Posti ,
A. Helmi  and
M. A. Breddels
Jovan Veljanoski
Affiliation:
Kapteyn Astronomical Institute, University of Groningen Landleven 12, 9747 AD Groningen, The Netherlands email: jovan@astro.rug.nl
L. Posti
Affiliation:
Kapteyn Astronomical Institute, University of Groningen Landleven 12, 9747 AD Groningen, The Netherlands email: jovan@astro.rug.nl
A. Helmi
Affiliation:
Kapteyn Astronomical Institute, University of Groningen Landleven 12, 9747 AD Groningen, The Netherlands email: jovan@astro.rug.nl
M. A. Breddels
Affiliation:
Kapteyn Astronomical Institute, University of Groningen Landleven 12, 9747 AD Groningen, The Netherlands email: jovan@astro.rug.nl
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Abstract

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The detailed study of the Galactic stellar halo may hold the key to unlocking the assembly history of the Milky Way. Here, we present a machine learning model for selecting metal poor stars from the TGAS catalogue using 5 dimensional phase-space information, coupled with optical and near-IR photometry. We characterise the degree of substructure in our halo sample in the Solar neighbourhood by measuring the velocity correlation function.

Keywords

Galaxy: haloGalaxy: structureGalaxy: kinematics and dynamics
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S330: Astrometry and Astrophysics in the Gaia sky , April 2017 , pp. 275 - 276
Copyright
Copyright © International Astronomical Union 2018 

References

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