GLOBULAR CLUSTER STREAMS AS GALACTIC HIGH-PRECISION SCALES-THE POSTER CHILD PALOMAR 5

Using the example of the tidal stream of the Milky Way globular cluster Palomar 5 (Pal 5), we demonstrate how observational data on tidal streams can be efficiently reduced in dimensionality and modeled in a Bayesian framework. Our approach combines detection of stream overdensities by a Difference-of-Gaussians process with fast streakline models of globular cluster streams and a continuous likelihood function built from these models. Inference is performed with Markov chain Monte Carlo. By generating approximate to 10(7) model streams, we show that the unique geometry of the Pal 5 debris yields powerful constraints on the solar position and motion, the Milky Way and Pal 5 itself. All 10 model parameters were allowed to vary over large ranges without additional prior information. Using only readily available SDSS data and a few radial velocities from the literature, we find that the distance of the Sun from the Galactic Center is 8.30 +/- 0.25 kpc, and the transverse velocity is 253 +/- 16 km s(-1). Both estimates are in excellent agreement with independent measurements of these two quantities. Assuming a standard disk and bulge model, we determine the Galactic mass within Pal 5's apogalactic radius of 19 kpc to be (2.1 +/- 0.4) x 10(11) M-circle dot. Moreover, we find the potential of the dark halo with a flattening of q(z) = 0.95(-0.12)(+0.16) to be essentially spherical-at least within the radial range that is effectively probed by Pal 5. We also determine Pal 5's mass, distance, and proper motion independently from other methods, which enables us to perform vital crosschecks. Our inferred heliocentric distance of Pal 5 is 23.6(-0.7)(+0.8) kpc, in perfect agreement with, and more precise than, estimates from isochrone fitting of deep Hubble Space Telescope (HST) imaging data. We conclude that finding and modeling more globular cluster streams is an efficient way to map out the structure of our Galaxy to high precision. With more observational data and by using additional prior information, the precision of this mapping can be significantly increased.