Machine Reading and Milky Model in the Gaia era

Lead Research Organisation: University College London
Department Name: Mullard Space Science Laboratory

Abstract

We will develop a tool to derive the Milky Way mass distribution with a Hierarchical Bayesian Model from the various kinds of observational constraints automatically extracted from the vast amount of literature published in the past with a novel machine reading tool. We will first train the machine to automatically extract the key observational measurements of the Milky Way, e.g. scale-length of the Galactic disk, from scientific articles. Then, we will feed them into the Milky Way mass model, statistically combining the literature values and their errors using Bayesian statistics, to derive the current best Milky Way model, including the mass distribution of the dark matter and stellar disk as a function of stellar populations. ESA's Gaia mission is going to make a full size dataset of position and velocity of more than one billion stars publicly available in April 2017. A large number of papers are expected to be published from such big data. The developed tool will be designed to use many pieces of scattered information, i.e. many individual publications by different groups, into a single unified statistically verified Milky Way model. We will compare the derived Milky Way models with the publications before and after the Gaia data, and evaluate the statistical impact of the Gaia mission.

Publications

10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
ST/N504488/1 01/10/2015 30/09/2020
2239649 Studentship ST/N504488/1 25/09/2017 31/03/2021 Thomas David Crossland
ST/R505171/1 01/10/2017 30/09/2021
2239649 Studentship ST/R505171/1 25/09/2017 31/03/2021 Thomas David Crossland