Extracting science from surveys of our Galaxy

TL;DR

This paper discusses the use of torus models to analyze large survey data of our Galaxy, enabling probabilistic understanding of its structure without direct inversion of observational data.

Contribution

It introduces the application of torus models for Galactic surveys, providing a new framework for probabilistic analysis of complex survey data.

Findings

Revised estimate of the Sun's velocity relative to the Local Standard of Rest.

Predicted variation of vertical velocity dispersion with distance from the Galactic plane.

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.