Extracting science from surveys of our Galaxy

Abstract

Our knowledge of the Galaxy is being revolutionised by a series of photometric, spectroscopic and astrometric surveys. Already an enormous body of data is available from completed surveys, and data of ever increasing quality and richness will accrue at least until the end of this decade. To extract science from these surveys we need a class of models that can give probability density functions in the space of the observables of a survey -- we should not attempt to "invert" the data from the space of observables into the physical space of the Galaxy. Currently just one class of model has the required capability, so-called "torus models". A pilot application of torus models to understanding the structure of the Galaxy's thin and thick discs has already produced two significant results: a major revision of our best estimate of the Sun's velocity with respect to the Local Standard of Rest, and a successful prediction of the way in which the vertical velocity dispersion in the disc varies with distance from the Galactic plane.

13 pages. Invited review to appear in Pramana - journal of physics (Indian Academy of Sciences)

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Cited by in corpus (8)

• Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy
• The Milky Way's Stellar Disk
• New distances to RAVE stars
• The distribution function of the Galaxy's dark halo
• Analysing surveys of our Galaxy -- II. Determining the potential
• 3D Extinction Mapping Using Hierarchical Bayesian Models
• Constraining the Galactic potential via action-based distribution functions for mono-abundance stellar populations
• Action-based Dynamical Modelling for the Milky Way Disk