Halo Star Streams in the Solar Neighborhood

Abstract

We have assembled a sample of halo stars in the solar neighborhood to look for halo substructure in velocity and angular momentum space. Our sample (231 stars) includes red giants, RR Lyrae variable stars, and red horizontal branch stars within 2.5 kpc of the Sun with [Fe/H] less than -1.0. It was chosen to include stars with accurate distances, space velocities, and metallicities, as well as well-quantified errors. With our data set, we confirm the existence of the streams found by Helmi and coworkers, which we refer to as the H99 streams. These streams have a double-peaked velocity distribution in the z -direction (out of the Galactic plane). We use the results of modeling of the H99 streams by Helmi and collaborators to test how one might use v z velocity information and radial velocity information to detect kinematic substructure in the halo. We find that detecting the H99 streams with radial velocities alone would require a large sample (e.g., approximately 150 stars within 2 kpc of the Sun and within 20° of the Galactic poles). In addition, we use the velocity distribution of the H99 streams to estimate their age. From our model of the progenitor of the H99 streams, we determine that it was accreted between 6 and 9 Gyr ago. The H99 streams have [α/Fe] abundances similar to other halo stars in the solar neighborhood, suggesting that the gas that formed these stars were enriched mostly by Type II supernovae. We have also discovered in angular momentum space two other possible substructures, which we refer to as the retrograde and prograde outliers. The retrograde outliers are likely to be halo substructure, but the prograde outliers are most likely part of the smooth halo. The retrograde outliers have significant structure in the v ##IMG## [http://ej.iop.org/icons/Entities/phi.gif] {phi} direction and show a range of [α/Fe], with two having low [α/Fe] for their [Fe/H]. The fraction of substructure stars in our sample is between 5% and 7%. The methods presented in this paper can be used to exploit the kinematic information present in future large databases like RAVE, SDSS-II/SEGUE, and Gaia .