We report the detection of a planetary companion around HIP 13044, a metal-poor red horizontal branch star belonging to a stellar halo stream that results from the disruption of an ancient Milky Way satellite galaxy. The detection is based on radial
velocity observations with FEROS at the 2.2-m MPG/ESO telescope. The periodic radial velocity variation of P=16.2 days can be distinguished from the periods of the stellar activity indicators. We computed a minimum planetary mass of 1.25 Jupiter masses and an orbital semimajor axis of 0.116 AU for the planet. This discovery is unique in three aspects: First, it is the first planet detection around a star with a metallicity much lower than few percent of the solar value; second, the planet host star resides in a stellar evolutionary stage that is still unexplored in the exoplanet surveys; third, the planetary system HIP 13044 most likely has an extragalactic origin in a disrupted former satellite of the Milky Way.
An efficient separation between dwarfs and giants in surveys of bright stars is important, especially for studies in which distances are estimated through photometric parallax relations. We use the available spectroscopic log g estimates from the sec
ond RAVE data release (DR2) to assign each star a probability for being a dwarf or subgiant/giant based on mixture model fits to the log g distribution in different color bins. We further attempt to use these stars as a labeled training set in order to classify stars which lack log g estimates into dwarfs and giants with a SVM algorithm. We assess the performance of this classification against different choices of the input feature vector. In particular, we use different combinations of reduced proper motions, 2MASS JHK, DENIS IJK and USNO-B B2R2 apparent magnitudes. Our study shows that -- for our color ranges -- the infrared bands alone provide no relevant information to separate dwarfs and giants. Even when optical bands and reduced proper motions are added, the fraction of true giants classified as dwarfs (the contamination) remains above 20%. Using only the dwarfs with available spectroscopic log g and distance estimates (the latter from Breddels et al. 2010), we then repeat the stream search by Klement, Fuchs & Rix (2008, KFR08), which assumed all stars were dwarfs and claimed the discovery of a new stellar stream at V = -160 km/s in a sample of 7015 stars from RAVE DR1. Our re-analysis of the pure DR2 dwarf sample exhibits an overdensity of 5 stars at the phase-space position of the KFR08 stream, with a metallicity distribution that appears inconsistent with that of stars at comparably low rotational velocities. Compared to several smooth Milky Way models, the mean standardized deviation of the KFR08 stream is only marginal at 1.6$pm$0.4... (abbreviated)
We test competing models that aim at explaining the nature of stars in the Milky Way that are well away (|z|$gtrsim$ 1kpc) from the midplane, the so-called thick disk: the stars may have gotten there through orbital migration, through satellite merge
rs and accretion, or through heating of pre-existing thin disk stars. Sales et al. (2009) proposed the eccentricity distribution of thick disk stars as a diagnostic to differentiate between these mechanisms. Drawing on SDSS DR7, we have assembled a sample of 34,223 G-dwarfs with 6-D phase-space information and metallicities, and have derived orbital eccentricities for them. Comparing the resulting eccentricity distributions, p(e|z), with the models, we find that: a) the observed p(e|z) is inconsistent with that predicted by orbital migration only, as there are more observed stars of high and of very low eccentricity; b) scenarios where the thick disk is made predominantly through abrupt heating of a pre-existing thin disk are also inconsistent, as they predict more high-eccentricity stars than observed; c) the observed p(e|z) fits well with a gas-rich merger scenario, where most thick disk stars were born from unsettled gas in situ.
We investigate the performance of some common machine learning techniques in identifying BHB stars from photometric data. To train the machine learning algorithms, we use previously published spectroscopic identifications of BHB stars from SDSS data.
We investigate the performance of three different techniques, namely k nearest neighbour classification, kernel density estimation and a support vector machine (SVM). We discuss the performance of the methods in terms of both completeness and contamination. We discuss the prospect of trading off these values, achieving lower contamination at the expense of lower completeness, by adjusting probability thresholds for the classification. We also discuss the role of prior probabilities in the classification performance, and we assess via simulations the reliability of the dataset used for training. Overall it seems that no-prior gives the best completeness, but adopting a prior lowers the contamination. We find that the SVM generally delivers the lowest contamination for a given level of completeness, and so is our method of choice. Finally, we classify a large sample of SDSS DR7 photometry using the SVM trained on the spectroscopic sample. We identify 27,074 probable BHB stars out of a sample of 294,652 stars. We derive photometric parallaxes and demonstrate that our results are reasonable by comparing to known distances for a selection of globular clusters. We attach our classifications, including probabilities, as an electronic table, so that they can be used either directly as a BHB star catalogue, or as priors to a spectroscopic or other classification method. We also provide our final models so that they can be directly applied to new data.
The phase-space structure of our Galaxy holds the key to understand and reconstruct its formation. The Lambda-CDM model predicts a richly structured phase-space distribution of dark matter and (halo) stars, consisting of streams of particles torn fro
m their progenitors during the process of hierarchical merging. While such streams quickly loose their spatial coherence in the process of phase mixing, the individual stars keep their common origin imprinted into their kinematic and chemical properties, allowing the recovery of the Galaxys individual building blocks. The field of Galactic Archeology has witnessed a dramatic boost over the last decade, thanks to the increasing quality and size of available data sets. This is especially true for the solar neighborhood, a volume of 1-2 kpc around the sun, where large scale surveys like SDSS/SEGUE continue to reveal the full 6D phase-space information of thousands of halo stars. In this review, I summarize the discoveries of stellar halo streams made so far and give a theoretical overview over the search strategies imployed. This paper is intended as an introduction to researchers new to field, but also as a reference illustrating the achievements made so far. I conclude that disentangling the individual fragments from which the Milky Way was built requires more precise data that will ultimately be delivered by the Gaia mission.
