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Gaia 1 and 2. a Pair of New Galactic Star Clusters MNRAS 000, 1{9 (2017) Preprint 15 May 2017 Compiled using MNRAS LATEX style file v3.0 Gaia 1 and 2. A pair of new Galactic star clusters Sergey E. Koposov,1;2? V. Belokurov,1 G. Torrealba1 1Institute of Astronomy, University of Cambridge, CB3 0HA 2McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA Accepted XXX. Received YYY; in original form ZZZ ABSTRACT We present the results of the very first search for faint Milky Way satellites in the Gaia data. Using stellar positions only, we are able to re-discover objects detected in much deeper data as recently as the last couple of years. While we do not identify new prominent ultra-faint dwarf galaxies, we report the discovery of two new star clusters, 4 Gaia 1 and Gaia 2. Gaia 1 is particularly curious, as it is a massive (2.2×10 M ), large (∼9 pc) and nearby (4.6 kpc) cluster, situated 100 away from the brightest star on the sky, Sirius! Even though this satellite is detected at significance in excess of 10, it was missed by previous sky surveys. We conclude that Gaia possesses powerful and unique capabilities for satellite detection thanks to its unrivalled angular resolution and highly efficient object classification. Key words: Galaxy: general { Galaxy: globular clusters { Galaxy: open clusters and associations { catalogues { Galaxy: structure 1 INTRODUCTION age resolution controls the limiting magnitude at which stars can be distinguished from galaxies with certainty. Of these In 1785, William Herschel introduced \star gauging" as a three factors above, only the camera properties are mostly technique to understand the shape of the Galaxy. Herschel invariant with time, while - as seen from the ground - the sky suggested that by splitting the heavens into cells and by is constantly changing. Thus, the faint star counts will fluc- counting the number of stars in each, it should be possible tuate as a function of the position on the sky in accordance to determine the position of the Sun within the Milky Way. with the survey progress. Around the limiting magnitude, Even though he promoted the method as \the most general the loss of genuine stars due to poor weather conditions is and the most proper", Herschel was quick to point out the exacerbated by the influx of spurious \stars", i.e. compact possibility of strong systematic effects given that different faint galaxies misclassified as stellar objects. At low Galactic telescopes would probe the skies to different depths. Over latitudes, diffraction spikes, blooming and ghosts produced the centuries, Herschel's prophecy of the merit of the tech- by bright stars also contribute large numbers of fictitious nique was confirmed, and most recently, the data from the \stars". As a result, typically, over-densities of bogus stellar all-sky surveys such as Two Micron All-Sky Survey (2MASS) objects caused by misclassified galaxies in galaxy clusters (Skrutskie et al. 2006) and Sloan Digital Sky Survey (SDSS) and by artifacts around bright stars outnumber bona-fide (York et al. 2000) has been used to show off its true power. satellites by several orders of magnitude. However, as the telescope-might grew and yet fainter stars Today, some 230 years after Herschel's first attempt, arXiv:1702.01122v2 [astro-ph.GA] 12 May 2017 were counted, the problem of minute but systematic depth variations blossomed into a major hindrance to studies of Gaia, the European Space Astrometric Mission, as part of the Milky Way structure. the Data Release 1 (DR1), has produced the most precise In the Galaxy, the one application that suffers the most Star Gage ever (Perryman et al. 2001; Gaia Collaboration from the non-uniform quality of the \star gage" is the search et al. 2016a,b). Positions, magnitudes and a wealth of addi- for the faint sub-structures in the stellar halo. Ultra-faint tional information on over a billion sources, as recorded in satellites and stellar streams appear as low-level enhance- the GaiaSource table, do not suffer from poor weather con- ments of the stellar density field and as such can either be ditions or dramatic sky brightness variations. Better still, destroyed or mimicked by the survey systematics. For exam- any spurious objects reported by the on-board detection al- ple, for a given exposure on a given telescope, a combination gorithm, are double-checked and discarded after subsequent of the atmosphere stability, the sky brightness and the im- visits. In terms of the star-galaxy separation, Gaia is truly unique. Its angular resolution is only approximately a factor of two worse than the resolution of the WFC3 camera on- ? E-mail: [email protected] board of the HST and Gaia can additionally discriminate c 2017 The Authors 2 S. Koposov et al. -3 7 Figure 1. The all-sky map of significances of stellar overdensities in Mollweide projection. The map was obtained using a 120 Gaussian kernel. between stars and galaxies based on the differences in their 2.2 Satellite search algorithm astrometric behaviour and spectrophotometry. Motivated by the unique properties of the Gaia DR1 data, here we inves- The principal ideas behind our Galactic satellite detection tigate the mission's capabilities for satellite detection. The algorithm have been extensively covered in the literature Paper is organised as follows. Section 2 presents the details (see e.g. Irwin 1994; Belokurov et al. 2007; Koposov et al. of the satellite detection algorithm as applied to the Gaia 2008a,b; Walsh et al. 2009; Willman 2010; Koposov et al. DR1 data. The Gaia discoveries are discussed in Section 3. 2015; Bechtol et al. 2015). However, the Gaia DR1 data rep- Section 4 studies the properties of the new satellites and resents a rather unusual and - in parts - challenging dataset Section 5 concludes the paper. to apply these simply \out of the box". This is primarily due to the absence of the object colour information, unless, of course, cross-matched with other surveys, which are usually either less deep than Gaia, such as 2MASS and APASS or do not cover the whole sky. Additionally, at magnitudes fainter than G = 20, the survey's depth starts to vary significantly as a function of the position on the sky. As a result, in this very early data release, the Gaia sky appears to look like 2 SATELLITE DETECTION WITH GAIA DR1 an intricate pattern of gaps of varying sizes (see e.g. Gaia Collaboration et al. 2016b). 2.1 GaiaSource all sky catalogue The combination of the factors above drove us to intro- The entirety of the analysis presented in this paper is based duce a slight modification to the stellar overdensity identifi- on the first Gaia data product released in late 2016, the cation method. We do use the kernel density estimation with GaiaSource table. For details on the contents and the prop- Gaussian kernels of varying width, however in order to assess erties of this catalogue we refer the reader to the Gaia Data the significance of the density deviation at any given point Release 1 paper (Gaia Collaboration et al. 2016b). Note on the sky, we do not rely on the Poisson distribution of the that in this paper the only quantities from the catalogue stellar number counts. Instead we obtain a local estimate of that we use are the stellar positions (RA and Dec), the G- the variance of the density. More precisely, if K(:; :) is the band magnitudes (van Leeuwen et al. 2017) and the values properly normalised density kernel on the sphere, H(α; δ) of the astrometric_excess_noise parameter (Lindegren et is the histogram of the star counts (on HEALPix grid on al. 2016). the sky, G´orski et al. 2005), the density estimate is sim- MNRAS 000, 1{9 (2017) Gaia 1 and 2 3 ply the convolution D(α; δ) = K ∗ H and the significance Ursa Minor I (S=32.8σ)[7,51] Canes Venatici I (S=9.1σ)[5,14] D <D> of the overdensity S(α; δ) = p− A , where the average V arA(D) 69 35 < D >A and the variance V arA(D) are calculated over the HEALPix neighbourhood (annulus) of a given point. Given 68 the normalisation by local variance, areas with pronounced 34 non-uniformities related to the Gaia scanning law, or regions [deg] 67 δ with large changes in extinction are naturally down-weighted 33 in their contribution to significance. 66 32 Given that the first Gaia DR does not provide colour information, we have to be particularly careful when select- 230 225 204 202 200 ing objects for the satellite search (we cannot use colour- Bootes I (S=6.7σ)[5,15] Reticulum 2 (S=6.3σ)[14,26] magnitude masks based on stellar isochrones, as in i.e. Ko- 52 posov et al. 2015). For the analysis in this paper we apply 16 − only two selection cuts. First cut concerns the Gaia G mag- 53 − nitude: 17 < G < 21. The reason for getting rid of bright 15 54 stars, i.e. those with G < 17 is twofold. First, for abso- [deg] − δ 14 lute majority of interesting targets at reasonable distances 55 from the Sun we expect the luminosity function of their stel- − 13 56 lar populations to rapidly rise with magnitude at G > 17. − Therefore, in these satellites (globular clusters and dwarf 212 210 208 57.5 55.0 52.5 50.0 galaxies), the number of stars fainter than G = 17 should α [deg] α [deg] vastly overwhelm the number of bright stars. Instead, the brighter magnitudes are dominated by the foreground con- Figure 2.
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